{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 文本分类任务"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6dc030b740319990"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## CNN——aclImbd"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ada8323efc5cd42a"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-11T02:26:24.549828Z",
     "start_time": "2025-06-11T02:26:23.652541Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import nltk\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "39bd45bd677afc9e"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 数据集路径\n",
    "aclImdb_data_dir = r'D:\\Machine_learning\\jiqixuexi\\aclImdb'\n",
    "\n",
    "# 定义设备\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# 自定义IMDb数据集类\n",
    "class CustomIMDbDataset(Dataset):\n",
    "    def __init__(self, data_dir, split):\n",
    "        self.data_dir = data_dir\n",
    "        self.split = split\n",
    "        self.samples = []\n",
    "        self.labels = []\n",
    "        self.tokenizer = nltk.word_tokenize\n",
    "        self.vocab = self.build_vocab()\n",
    "\n",
    "        # 加载数据\n",
    "        for label in ['pos', 'neg']:\n",
    "            label_dir = os.path.join(data_dir, split, label)\n",
    "            for file_name in os.listdir(label_dir):\n",
    "                file_path = os.path.join(label_dir, file_name)\n",
    "                with open(file_path, 'r', encoding='utf-8') as f:\n",
    "                    text = f.read()\n",
    "                self.samples.append(text)\n",
    "                self.labels.append(1 if label == 'pos' else 0)\n",
    "\n",
    "    def build_vocab(self):\n",
    "        vocab = {}\n",
    "        for label in ['pos', 'neg']:\n",
    "            label_dir = os.path.join(self.data_dir, self.split, label)\n",
    "            for file_name in os.listdir(label_dir):\n",
    "                file_path = os.path.join(label_dir, file_name)\n",
    "                with open(file_path, 'r', encoding='utf-8') as f:\n",
    "                    text = f.read()\n",
    "                tokens = self.tokenizer(text.lower())\n",
    "                for token in tokens:\n",
    "                    if token not in vocab:\n",
    "                        vocab[token] = len(vocab)\n",
    "        return vocab\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.samples)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        text = self.samples[idx]\n",
    "        label = self.labels[idx]\n",
    "\n",
    "        # 文本处理\n",
    "        tokens = self.tokenizer(text.lower())\n",
    "        indices = [self.vocab[token] if token in self.vocab else len(self.vocab) for token in tokens]\n",
    "        tensor = torch.tensor(indices, dtype=torch.long)\n",
    "\n",
    "        return tensor, label\n",
    "    # 定义CNN模型用于文本分类\n",
    "class TextCNN(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim, kernel_sizes, num_channels, num_classes):\n",
    "        super(TextCNN, self).__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size + 1, embedding_dim)\n",
    "        self.convs = nn.ModuleList([\n",
    "            nn.Conv1d(in_channels=embedding_dim, out_channels=num_channels, kernel_size=ks)\n",
    "            for ks in kernel_sizes\n",
    "        ])\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.fc = nn.Linear(num_channels * len(kernel_sizes), num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x: (batch_size, seq_len)\n",
    "        x = self.embedding(x)  # (batch_size, seq_len, embedding_dim)\n",
    "        x = x.permute(0, 2, 1)  # (batch_size, embedding_dim, seq_len)\n",
    "        outputs = []\n",
    "        for conv in self.convs:\n",
    "            output = conv(x)  # (batch_size, num_channels, seq_len - kernel_size + 1)\n",
    "            output = torch.relu(output)\n",
    "            output = torch.max(output, dim=2)[0]  # (batch_size, num_channels)\n",
    "            outputs.append(output)\n",
    "        x = torch.cat(outputs, dim=1)  # (batch_size, num_channels * len(kernel_sizes))\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "# 加载数据\n",
    "def load_data(data_dir, split, batch_size):\n",
    "    dataset = CustomIMDbDataset(data_dir, split)\n",
    "    data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=lambda batch: batch)\n",
    "    return data_loader"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-11T02:27:01.665320Z",
     "start_time": "2025-06-11T02:27:01.654285Z"
    }
   },
   "id": "fcaa36ba56cc61af",
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Step: 1, Loss: 1.6992\n",
      "Train Step: 2, Loss: 1.1676\n",
      "Train Step: 3, Loss: 0.8764\n",
      "Train Step: 4, Loss: 1.1223\n",
      "Train Step: 5, Loss: 1.1447\n",
      "Train Step: 6, Loss: 1.2102\n",
      "Train Step: 7, Loss: 1.1312\n",
      "Train Step: 8, Loss: 1.1708\n",
      "Train Step: 9, Loss: 0.9448\n",
      "Train Step: 10, Loss: 0.9987\n",
      "Train Step: 11, Loss: 1.0839\n",
      "Train Step: 12, Loss: 0.8859\n",
      "Train Step: 13, Loss: 1.2350\n",
      "Train Step: 14, Loss: 0.9805\n",
      "Train Step: 15, Loss: 0.9182\n",
      "Train Step: 16, Loss: 0.6466\n",
      "Train Step: 17, Loss: 0.9022\n",
      "Train Step: 18, Loss: 0.8424\n",
      "Train Step: 19, Loss: 0.8323\n",
      "Train Step: 20, Loss: 0.8542\n",
      "Train Step: 21, Loss: 0.9650\n",
      "Train Step: 22, Loss: 0.8164\n",
      "Train Step: 23, Loss: 0.9442\n",
      "Train Step: 24, Loss: 0.7513\n",
      "Train Step: 25, Loss: 0.8570\n",
      "Train Step: 26, Loss: 0.7801\n",
      "Train Step: 27, Loss: 0.9744\n",
      "Train Step: 28, Loss: 0.8165\n",
      "Train Step: 29, Loss: 0.8585\n",
      "Train Step: 30, Loss: 0.8877\n",
      "Train Step: 31, Loss: 0.8473\n",
      "Train Step: 32, Loss: 0.7258\n",
      "Train Step: 33, Loss: 0.8932\n",
      "Train Step: 34, Loss: 0.7708\n",
      "Train Step: 35, Loss: 0.8437\n",
      "Train Step: 36, Loss: 0.9137\n",
      "Train Step: 37, Loss: 0.7127\n",
      "Train Step: 38, Loss: 0.8311\n",
      "Train Step: 39, Loss: 0.7239\n",
      "Train Step: 40, Loss: 0.9198\n",
      "Train Step: 41, Loss: 0.7233\n",
      "Train Step: 42, Loss: 0.6783\n",
      "Train Step: 43, Loss: 0.7376\n",
      "Train Step: 44, Loss: 0.8474\n",
      "Train Step: 45, Loss: 0.8286\n",
      "Train Step: 46, Loss: 0.7754\n",
      "Train Step: 47, Loss: 0.8302\n",
      "Train Step: 48, Loss: 0.6495\n",
      "Train Step: 49, Loss: 0.7424\n",
      "Train Step: 50, Loss: 0.6952\n",
      "Train Step: 51, Loss: 0.8345\n",
      "Train Step: 52, Loss: 0.7475\n",
      "Train Step: 53, Loss: 0.8728\n",
      "Train Step: 54, Loss: 0.9388\n",
      "Train Step: 55, Loss: 0.7943\n",
      "Train Step: 56, Loss: 0.8146\n",
      "Train Step: 57, Loss: 0.8269\n",
      "Train Step: 58, Loss: 0.7699\n",
      "Train Step: 59, Loss: 0.6576\n",
      "Train Step: 60, Loss: 0.8089\n",
      "Train Step: 61, Loss: 0.7209\n",
      "Train Step: 62, Loss: 0.7515\n",
      "Train Step: 63, Loss: 0.6902\n",
      "Train Step: 64, Loss: 0.8320\n",
      "Train Step: 65, Loss: 0.7419\n",
      "Train Step: 66, Loss: 0.6875\n",
      "Train Step: 67, Loss: 0.7117\n",
      "Train Step: 68, Loss: 0.7452\n",
      "Train Step: 69, Loss: 0.7186\n",
      "Train Step: 70, Loss: 0.7556\n",
      "Train Step: 71, Loss: 0.8704\n",
      "Train Step: 72, Loss: 0.7271\n",
      "Train Step: 73, Loss: 0.7107\n",
      "Train Step: 74, Loss: 0.7218\n",
      "Train Step: 75, Loss: 0.6697\n",
      "Train Step: 76, Loss: 0.6980\n",
      "Train Step: 77, Loss: 0.7457\n",
      "Train Step: 78, Loss: 0.8405\n",
      "Train Step: 79, Loss: 0.7290\n",
      "Train Step: 80, Loss: 0.7290\n",
      "Train Step: 81, Loss: 0.6638\n",
      "Train Step: 82, Loss: 0.6991\n",
      "Train Step: 83, Loss: 0.7204\n",
      "Train Step: 84, Loss: 0.7007\n",
      "Train Step: 85, Loss: 0.6848\n",
      "Train Step: 86, Loss: 0.6785\n",
      "Train Step: 87, Loss: 0.6955\n",
      "Train Step: 88, Loss: 0.6551\n",
      "Train Step: 89, Loss: 0.6533\n",
      "Train Step: 90, Loss: 0.7277\n",
      "Train Step: 91, Loss: 0.7620\n",
      "Train Step: 92, Loss: 0.7250\n",
      "Train Step: 93, Loss: 0.6729\n",
      "Train Step: 94, Loss: 0.7343\n",
      "Train Step: 95, Loss: 0.6673\n",
      "Train Step: 96, Loss: 0.7024\n",
      "Train Step: 97, Loss: 0.6799\n",
      "Train Step: 98, Loss: 0.6970\n",
      "Train Step: 99, Loss: 0.6730\n",
      "Train Step: 100, Loss: 0.6705\n",
      "Train Step: 101, Loss: 0.7093\n",
      "Train Step: 102, Loss: 0.7182\n",
      "Train Step: 103, Loss: 0.6501\n",
      "Train Step: 104, Loss: 0.8127\n",
      "Train Step: 105, Loss: 0.6794\n",
      "Train Step: 106, Loss: 0.6659\n",
      "Train Step: 107, Loss: 0.7002\n",
      "Train Step: 108, Loss: 0.7692\n",
      "Train Step: 109, Loss: 0.7222\n",
      "Train Step: 110, Loss: 0.7324\n",
      "Train Step: 111, Loss: 0.6950\n",
      "Train Step: 112, Loss: 0.6877\n",
      "Train Step: 113, Loss: 0.7299\n",
      "Train Step: 114, Loss: 0.7561\n",
      "Train Step: 115, Loss: 0.6487\n",
      "Train Step: 116, Loss: 0.7336\n",
      "Train Step: 117, Loss: 0.8166\n",
      "Train Step: 118, Loss: 0.6169\n",
      "Train Step: 119, Loss: 0.7018\n",
      "Train Step: 120, Loss: 0.6091\n",
      "Train Step: 121, Loss: 0.6791\n",
      "Train Step: 122, Loss: 0.7445\n",
      "Train Step: 123, Loss: 0.6887\n",
      "Train Step: 124, Loss: 0.5635\n",
      "Train Step: 125, Loss: 0.6798\n",
      "Train Step: 126, Loss: 0.7390\n",
      "Train Step: 127, Loss: 0.7638\n",
      "Train Step: 128, Loss: 0.6719\n",
      "Train Step: 129, Loss: 0.5587\n",
      "Train Step: 130, Loss: 0.7497\n",
      "Train Step: 131, Loss: 0.6068\n",
      "Train Step: 132, Loss: 0.6770\n",
      "Train Step: 133, Loss: 0.5452\n",
      "Train Step: 134, Loss: 0.6925\n",
      "Train Step: 135, Loss: 0.5867\n",
      "Train Step: 136, Loss: 0.7165\n",
      "Train Step: 137, Loss: 0.6654\n",
      "Train Step: 138, Loss: 0.6405\n",
      "Train Step: 139, Loss: 0.8007\n",
      "Train Step: 140, Loss: 0.5231\n",
      "Train Step: 141, Loss: 0.5724\n",
      "Train Step: 142, Loss: 0.6310\n",
      "Train Step: 143, Loss: 0.6391\n",
      "Train Step: 144, Loss: 0.7389\n",
      "Train Step: 145, Loss: 0.6826\n",
      "Train Step: 146, Loss: 0.6253\n",
      "Train Step: 147, Loss: 0.5635\n",
      "Train Step: 148, Loss: 0.5797\n",
      "Train Step: 149, Loss: 0.6902\n",
      "Train Step: 150, Loss: 0.6752\n",
      "Train Step: 151, Loss: 0.6673\n",
      "Train Step: 152, Loss: 0.6561\n",
      "Train Step: 153, Loss: 0.6723\n",
      "Train Step: 154, Loss: 0.5604\n",
      "Train Step: 155, Loss: 0.6546\n",
      "Train Step: 156, Loss: 0.6610\n",
      "Train Step: 157, Loss: 0.6954\n",
      "Train Step: 158, Loss: 0.6552\n",
      "Train Step: 159, Loss: 0.6077\n",
      "Train Step: 160, Loss: 0.7557\n",
      "Train Step: 161, Loss: 0.6815\n",
      "Train Step: 162, Loss: 0.7163\n",
      "Train Step: 163, Loss: 0.6529\n",
      "Train Step: 164, Loss: 0.6814\n",
      "Train Step: 165, Loss: 0.6540\n",
      "Train Step: 166, Loss: 0.6397\n",
      "Train Step: 167, Loss: 0.5993\n",
      "Train Step: 168, Loss: 0.6619\n",
      "Train Step: 169, Loss: 0.6978\n",
      "Train Step: 170, Loss: 0.7567\n",
      "Train Step: 171, Loss: 0.6214\n",
      "Train Step: 172, Loss: 0.5540\n",
      "Train Step: 173, Loss: 0.6055\n",
      "Train Step: 174, Loss: 0.6374\n",
      "Train Step: 175, Loss: 0.7428\n",
      "Train Step: 176, Loss: 0.8039\n",
      "Train Step: 177, Loss: 0.6309\n",
      "Train Step: 178, Loss: 0.7440\n",
      "Train Step: 179, Loss: 0.5992\n",
      "Train Step: 180, Loss: 0.6179\n",
      "Train Step: 181, Loss: 0.7263\n",
      "Train Step: 182, Loss: 0.6763\n",
      "Train Step: 183, Loss: 0.6696\n",
      "Train Step: 184, Loss: 0.7134\n",
      "Train Step: 185, Loss: 0.7087\n",
      "Train Step: 186, Loss: 0.6856\n",
      "Train Step: 187, Loss: 0.6584\n",
      "Train Step: 188, Loss: 0.5498\n",
      "Train Step: 189, Loss: 0.5887\n",
      "Train Step: 190, Loss: 0.7194\n",
      "Train Step: 191, Loss: 0.5921\n",
      "Train Step: 192, Loss: 0.7326\n",
      "Train Step: 193, Loss: 0.7079\n",
      "Train Step: 194, Loss: 0.7609\n",
      "Train Step: 195, Loss: 0.5593\n",
      "Train Step: 196, Loss: 0.5485\n",
      "Train Step: 197, Loss: 0.6620\n",
      "Train Step: 198, Loss: 0.7260\n",
      "Train Step: 199, Loss: 0.7179\n",
      "Train Step: 200, Loss: 0.6721\n",
      "Train Step: 201, Loss: 0.5317\n",
      "Train Step: 202, Loss: 0.7004\n",
      "Train Step: 203, Loss: 0.7430\n",
      "Train Step: 204, Loss: 0.8173\n",
      "Train Step: 205, Loss: 0.8360\n",
      "Train Step: 206, Loss: 0.5736\n",
      "Train Step: 207, Loss: 0.6518\n",
      "Train Step: 208, Loss: 0.6081\n",
      "Train Step: 209, Loss: 0.6208\n",
      "Train Step: 210, Loss: 0.5483\n",
      "Train Step: 211, Loss: 0.6753\n",
      "Train Step: 212, Loss: 0.5194\n",
      "Train Step: 213, Loss: 0.7005\n",
      "Train Step: 214, Loss: 0.6301\n",
      "Train Step: 215, Loss: 0.5413\n",
      "Train Step: 216, Loss: 0.5726\n",
      "Train Step: 217, Loss: 0.6152\n",
      "Train Step: 218, Loss: 0.7002\n",
      "Train Step: 219, Loss: 0.6343\n",
      "Train Step: 220, Loss: 0.5693\n",
      "Train Step: 221, Loss: 0.4728\n",
      "Train Step: 222, Loss: 0.5461\n",
      "Train Step: 223, Loss: 0.6210\n",
      "Train Step: 224, Loss: 0.6364\n",
      "Train Step: 225, Loss: 0.6653\n",
      "Train Step: 226, Loss: 0.5955\n",
      "Train Step: 227, Loss: 0.5063\n",
      "Train Step: 228, Loss: 0.6517\n",
      "Train Step: 229, Loss: 0.5394\n",
      "Train Step: 230, Loss: 0.6895\n",
      "Train Step: 231, Loss: 0.4924\n",
      "Train Step: 232, Loss: 0.6587\n",
      "Train Step: 233, Loss: 0.6198\n",
      "Train Step: 234, Loss: 0.6575\n",
      "Train Step: 235, Loss: 0.6668\n",
      "Train Step: 236, Loss: 0.7181\n",
      "Train Step: 237, Loss: 0.5320\n",
      "Train Step: 238, Loss: 0.6193\n",
      "Train Step: 239, Loss: 0.5398\n",
      "Train Step: 240, Loss: 0.6423\n",
      "Train Step: 241, Loss: 0.7635\n",
      "Train Step: 242, Loss: 0.7633\n",
      "Train Step: 243, Loss: 0.5375\n",
      "Train Step: 244, Loss: 0.5360\n",
      "Train Step: 245, Loss: 0.5396\n",
      "Train Step: 246, Loss: 0.6407\n",
      "Train Step: 247, Loss: 0.5317\n",
      "Train Step: 248, Loss: 0.5253\n",
      "Train Step: 249, Loss: 0.5116\n",
      "Train Step: 250, Loss: 0.7814\n",
      "Train Step: 251, Loss: 0.7565\n",
      "Train Step: 252, Loss: 0.6586\n",
      "Train Step: 253, Loss: 0.5367\n",
      "Train Step: 254, Loss: 0.5278\n",
      "Train Step: 255, Loss: 0.6055\n",
      "Train Step: 256, Loss: 0.6654\n",
      "Train Step: 257, Loss: 0.5904\n",
      "Train Step: 258, Loss: 0.7238\n",
      "Train Step: 259, Loss: 0.4400\n",
      "Train Step: 260, Loss: 0.5366\n",
      "Train Step: 261, Loss: 0.6040\n",
      "Train Step: 262, Loss: 0.6344\n",
      "Train Step: 263, Loss: 0.6283\n",
      "Train Step: 264, Loss: 0.6405\n",
      "Train Step: 265, Loss: 0.4664\n",
      "Train Step: 266, Loss: 0.5864\n",
      "Train Step: 267, Loss: 0.5313\n",
      "Train Step: 268, Loss: 0.6165\n",
      "Train Step: 269, Loss: 0.5101\n",
      "Train Step: 270, Loss: 0.5442\n",
      "Train Step: 271, Loss: 0.5737\n",
      "Train Step: 272, Loss: 0.5978\n",
      "Train Step: 273, Loss: 0.5823\n",
      "Train Step: 274, Loss: 0.5730\n",
      "Train Step: 275, Loss: 0.5455\n",
      "Train Step: 276, Loss: 0.5618\n",
      "Train Step: 277, Loss: 0.6660\n",
      "Train Step: 278, Loss: 0.5507\n",
      "Train Step: 279, Loss: 0.5854\n",
      "Train Step: 280, Loss: 0.5854\n",
      "Train Step: 281, Loss: 0.5745\n",
      "Train Step: 282, Loss: 0.5923\n",
      "Train Step: 283, Loss: 0.5994\n",
      "Train Step: 284, Loss: 0.6128\n",
      "Train Step: 285, Loss: 0.6060\n",
      "Train Step: 286, Loss: 0.6191\n",
      "Train Step: 287, Loss: 0.6261\n",
      "Train Step: 288, Loss: 0.4983\n",
      "Train Step: 289, Loss: 0.5484\n",
      "Train Step: 290, Loss: 0.5447\n",
      "Train Step: 291, Loss: 0.5949\n",
      "Train Step: 292, Loss: 0.5660\n",
      "Train Step: 293, Loss: 0.5351\n",
      "Train Step: 294, Loss: 0.5307\n",
      "Train Step: 295, Loss: 0.5566\n",
      "Train Step: 296, Loss: 0.5730\n",
      "Train Step: 297, Loss: 0.5530\n",
      "Train Step: 298, Loss: 0.6801\n",
      "Train Step: 299, Loss: 0.5133\n",
      "Train Step: 300, Loss: 0.5604\n",
      "Train Step: 301, Loss: 0.6108\n",
      "Train Step: 302, Loss: 0.4569\n",
      "Train Step: 303, Loss: 0.4971\n",
      "Train Step: 304, Loss: 0.5542\n",
      "Train Step: 305, Loss: 0.5351\n",
      "Train Step: 306, Loss: 0.5625\n",
      "Train Step: 307, Loss: 0.5523\n",
      "Train Step: 308, Loss: 0.5949\n",
      "Train Step: 309, Loss: 0.6237\n",
      "Train Step: 310, Loss: 0.5846\n",
      "Train Step: 311, Loss: 0.4699\n",
      "Train Step: 312, Loss: 0.4509\n",
      "Train Step: 313, Loss: 0.5579\n",
      "Train Step: 314, Loss: 0.4391\n",
      "Train Step: 315, Loss: 0.5782\n",
      "Train Step: 316, Loss: 0.5340\n",
      "Train Step: 317, Loss: 0.5363\n",
      "Train Step: 318, Loss: 0.6410\n",
      "Train Step: 319, Loss: 0.4681\n",
      "Train Step: 320, Loss: 0.5607\n",
      "Train Step: 321, Loss: 0.5252\n",
      "Train Step: 322, Loss: 0.6727\n",
      "Train Step: 323, Loss: 0.6628\n",
      "Train Step: 324, Loss: 0.4933\n",
      "Train Step: 325, Loss: 0.6083\n",
      "Train Step: 326, Loss: 0.5204\n",
      "Train Step: 327, Loss: 0.6290\n",
      "Train Step: 328, Loss: 0.4946\n",
      "Train Step: 329, Loss: 0.6156\n",
      "Train Step: 330, Loss: 0.5853\n",
      "Train Step: 331, Loss: 0.6373\n",
      "Train Step: 332, Loss: 0.5965\n",
      "Train Step: 333, Loss: 0.5178\n",
      "Train Step: 334, Loss: 0.6493\n",
      "Train Step: 335, Loss: 0.5249\n",
      "Train Step: 336, Loss: 0.5746\n",
      "Train Step: 337, Loss: 0.3771\n",
      "Train Step: 338, Loss: 0.5564\n",
      "Train Step: 339, Loss: 0.5147\n",
      "Train Step: 340, Loss: 0.6966\n",
      "Train Step: 341, Loss: 0.5842\n",
      "Train Step: 342, Loss: 0.6570\n",
      "Train Step: 343, Loss: 0.5399\n",
      "Train Step: 344, Loss: 0.5715\n",
      "Train Step: 345, Loss: 0.4624\n",
      "Train Step: 346, Loss: 0.6092\n",
      "Train Step: 347, Loss: 0.6514\n",
      "Train Step: 348, Loss: 0.6568\n",
      "Train Step: 349, Loss: 0.4450\n",
      "Train Step: 350, Loss: 0.6811\n",
      "Train Step: 351, Loss: 0.5690\n",
      "Train Step: 352, Loss: 0.6352\n",
      "Train Step: 353, Loss: 0.4820\n",
      "Train Step: 354, Loss: 0.5197\n",
      "Train Step: 355, Loss: 0.5827\n",
      "Train Step: 356, Loss: 0.5271\n",
      "Train Step: 357, Loss: 0.6435\n",
      "Train Step: 358, Loss: 0.5872\n",
      "Train Step: 359, Loss: 0.5484\n",
      "Train Step: 360, Loss: 0.5985\n",
      "Train Step: 361, Loss: 0.4798\n",
      "Train Step: 362, Loss: 0.5172\n",
      "Train Step: 363, Loss: 0.5650\n",
      "Train Step: 364, Loss: 0.5779\n",
      "Train Step: 365, Loss: 0.5910\n",
      "Train Step: 366, Loss: 0.5134\n",
      "Train Step: 367, Loss: 0.5140\n",
      "Train Step: 368, Loss: 0.5104\n",
      "Train Step: 369, Loss: 0.5293\n",
      "Train Step: 370, Loss: 0.5684\n",
      "Train Step: 371, Loss: 0.5069\n",
      "Train Step: 372, Loss: 0.5828\n",
      "Train Step: 373, Loss: 0.4569\n",
      "Train Step: 374, Loss: 0.5528\n",
      "Train Step: 375, Loss: 0.4588\n",
      "Train Step: 376, Loss: 0.5475\n",
      "Train Step: 377, Loss: 0.5552\n",
      "Train Step: 378, Loss: 0.5955\n",
      "Train Step: 379, Loss: 0.6214\n",
      "Train Step: 380, Loss: 0.4910\n",
      "Train Step: 381, Loss: 0.7201\n",
      "Train Step: 382, Loss: 0.5168\n",
      "Train Step: 383, Loss: 0.5299\n",
      "Train Step: 384, Loss: 0.6383\n",
      "Train Step: 385, Loss: 0.4228\n",
      "Train Step: 386, Loss: 0.5359\n",
      "Train Step: 387, Loss: 0.5349\n",
      "Train Step: 388, Loss: 0.6109\n",
      "Train Step: 389, Loss: 0.6226\n",
      "Train Step: 390, Loss: 0.5063\n",
      "Train Step: 391, Loss: 0.5882\n",
      "Epoch [1/10], Loss: 0.6642\n",
      "Train Step: 392, Loss: 0.5099\n",
      "Train Step: 393, Loss: 0.4853\n",
      "Train Step: 394, Loss: 0.5186\n",
      "Train Step: 395, Loss: 0.6118\n",
      "Train Step: 396, Loss: 0.5350\n",
      "Train Step: 397, Loss: 0.4776\n",
      "Train Step: 398, Loss: 0.5301\n",
      "Train Step: 399, Loss: 0.5025\n",
      "Train Step: 400, Loss: 0.4641\n",
      "Train Step: 401, Loss: 0.5186\n",
      "Train Step: 402, Loss: 0.5341\n",
      "Train Step: 403, Loss: 0.4608\n",
      "Train Step: 404, Loss: 0.5623\n",
      "Train Step: 405, Loss: 0.5917\n",
      "Train Step: 406, Loss: 0.5441\n",
      "Train Step: 407, Loss: 0.5036\n",
      "Train Step: 408, Loss: 0.4973\n",
      "Train Step: 409, Loss: 0.4595\n",
      "Train Step: 410, Loss: 0.5727\n",
      "Train Step: 411, Loss: 0.4384\n",
      "Train Step: 412, Loss: 0.6046\n",
      "Train Step: 413, Loss: 0.6438\n",
      "Train Step: 414, Loss: 0.5346\n",
      "Train Step: 415, Loss: 0.4675\n",
      "Train Step: 416, Loss: 0.4974\n",
      "Train Step: 417, Loss: 0.5943\n",
      "Train Step: 418, Loss: 0.5661\n",
      "Train Step: 419, Loss: 0.5707\n",
      "Train Step: 420, Loss: 0.4931\n",
      "Train Step: 421, Loss: 0.4963\n",
      "Train Step: 422, Loss: 0.6753\n",
      "Train Step: 423, Loss: 0.5027\n",
      "Train Step: 424, Loss: 0.6817\n",
      "Train Step: 425, Loss: 0.5069\n",
      "Train Step: 426, Loss: 0.5042\n",
      "Train Step: 427, Loss: 0.4037\n",
      "Train Step: 428, Loss: 0.6463\n",
      "Train Step: 429, Loss: 0.4851\n",
      "Train Step: 430, Loss: 0.5254\n",
      "Train Step: 431, Loss: 0.4483\n",
      "Train Step: 432, Loss: 0.6984\n",
      "Train Step: 433, Loss: 0.5136\n",
      "Train Step: 434, Loss: 0.4886\n",
      "Train Step: 435, Loss: 0.5151\n",
      "Train Step: 436, Loss: 0.4578\n",
      "Train Step: 437, Loss: 0.5019\n",
      "Train Step: 438, Loss: 0.5365\n",
      "Train Step: 439, Loss: 0.6207\n",
      "Train Step: 440, Loss: 0.5060\n",
      "Train Step: 441, Loss: 0.6446\n",
      "Train Step: 442, Loss: 0.5609\n",
      "Train Step: 443, Loss: 0.5439\n",
      "Train Step: 444, Loss: 0.5976\n",
      "Train Step: 445, Loss: 0.4370\n",
      "Train Step: 446, Loss: 0.5153\n",
      "Train Step: 447, Loss: 0.5329\n",
      "Train Step: 448, Loss: 0.4914\n",
      "Train Step: 449, Loss: 0.3999\n",
      "Train Step: 450, Loss: 0.5208\n",
      "Train Step: 451, Loss: 0.3918\n",
      "Train Step: 452, Loss: 0.4946\n",
      "Train Step: 453, Loss: 0.4316\n",
      "Train Step: 454, Loss: 0.4660\n",
      "Train Step: 455, Loss: 0.4596\n",
      "Train Step: 456, Loss: 0.5104\n",
      "Train Step: 457, Loss: 0.4681\n",
      "Train Step: 458, Loss: 0.4772\n",
      "Train Step: 459, Loss: 0.4882\n",
      "Train Step: 460, Loss: 0.5246\n",
      "Train Step: 461, Loss: 0.4688\n",
      "Train Step: 462, Loss: 0.5157\n",
      "Train Step: 463, Loss: 0.4512\n",
      "Train Step: 464, Loss: 0.4988\n",
      "Train Step: 465, Loss: 0.4262\n",
      "Train Step: 466, Loss: 0.6319\n",
      "Train Step: 467, Loss: 0.5622\n",
      "Train Step: 468, Loss: 0.4435\n",
      "Train Step: 469, Loss: 0.6511\n",
      "Train Step: 470, Loss: 0.5479\n",
      "Train Step: 471, Loss: 0.5347\n",
      "Train Step: 472, Loss: 0.4455\n",
      "Train Step: 473, Loss: 0.4672\n",
      "Train Step: 474, Loss: 0.5087\n",
      "Train Step: 475, Loss: 0.4042\n",
      "Train Step: 476, Loss: 0.4290\n",
      "Train Step: 477, Loss: 0.4650\n",
      "Train Step: 478, Loss: 0.5314\n",
      "Train Step: 479, Loss: 0.4337\n",
      "Train Step: 480, Loss: 0.4978\n",
      "Train Step: 481, Loss: 0.3954\n",
      "Train Step: 482, Loss: 0.5800\n",
      "Train Step: 483, Loss: 0.5104\n",
      "Train Step: 484, Loss: 0.5779\n",
      "Train Step: 485, Loss: 0.5004\n",
      "Train Step: 486, Loss: 0.5138\n",
      "Train Step: 487, Loss: 0.5078\n",
      "Train Step: 488, Loss: 0.4793\n",
      "Train Step: 489, Loss: 0.5273\n",
      "Train Step: 490, Loss: 0.3879\n",
      "Train Step: 491, Loss: 0.4992\n",
      "Train Step: 492, Loss: 0.5655\n",
      "Train Step: 493, Loss: 0.4787\n",
      "Train Step: 494, Loss: 0.5820\n",
      "Train Step: 495, Loss: 0.4391\n",
      "Train Step: 496, Loss: 0.4556\n",
      "Train Step: 497, Loss: 0.5486\n",
      "Train Step: 498, Loss: 0.4571\n",
      "Train Step: 499, Loss: 0.5132\n",
      "Train Step: 500, Loss: 0.5434\n",
      "Train Step: 501, Loss: 0.5162\n",
      "Train Step: 502, Loss: 0.4633\n",
      "Train Step: 503, Loss: 0.5457\n",
      "Train Step: 504, Loss: 0.5354\n",
      "Train Step: 505, Loss: 0.4647\n",
      "Train Step: 506, Loss: 0.4333\n",
      "Train Step: 507, Loss: 0.4628\n",
      "Train Step: 508, Loss: 0.5108\n",
      "Train Step: 509, Loss: 0.5270\n",
      "Train Step: 510, Loss: 0.4407\n",
      "Train Step: 511, Loss: 0.4425\n",
      "Train Step: 512, Loss: 0.4637\n",
      "Train Step: 513, Loss: 0.4472\n",
      "Train Step: 514, Loss: 0.4400\n",
      "Train Step: 515, Loss: 0.4936\n",
      "Train Step: 516, Loss: 0.4190\n",
      "Train Step: 517, Loss: 0.5564\n",
      "Train Step: 518, Loss: 0.4777\n",
      "Train Step: 519, Loss: 0.4735\n",
      "Train Step: 520, Loss: 0.4241\n",
      "Train Step: 521, Loss: 0.6266\n",
      "Train Step: 522, Loss: 0.4846\n",
      "Train Step: 523, Loss: 0.4447\n",
      "Train Step: 524, Loss: 0.5017\n",
      "Train Step: 525, Loss: 0.6426\n",
      "Train Step: 526, Loss: 0.4522\n",
      "Train Step: 527, Loss: 0.4717\n",
      "Train Step: 528, Loss: 0.3786\n",
      "Train Step: 529, Loss: 0.4858\n",
      "Train Step: 530, Loss: 0.6712\n",
      "Train Step: 531, Loss: 0.5807\n",
      "Train Step: 532, Loss: 0.4575\n",
      "Train Step: 533, Loss: 0.3755\n",
      "Train Step: 534, Loss: 0.4597\n",
      "Train Step: 535, Loss: 0.4297\n",
      "Train Step: 536, Loss: 0.6117\n",
      "Train Step: 537, Loss: 0.6256\n",
      "Train Step: 538, Loss: 0.4873\n",
      "Train Step: 539, Loss: 0.3161\n",
      "Train Step: 540, Loss: 0.4808\n",
      "Train Step: 541, Loss: 0.4330\n",
      "Train Step: 542, Loss: 0.5269\n",
      "Train Step: 543, Loss: 0.4759\n",
      "Train Step: 544, Loss: 0.6486\n",
      "Train Step: 545, Loss: 0.5295\n",
      "Train Step: 546, Loss: 0.3877\n",
      "Train Step: 547, Loss: 0.3775\n",
      "Train Step: 548, Loss: 0.3830\n",
      "Train Step: 549, Loss: 0.3845\n",
      "Train Step: 550, Loss: 0.4572\n",
      "Train Step: 551, Loss: 0.4962\n",
      "Train Step: 552, Loss: 0.4378\n",
      "Train Step: 553, Loss: 0.4888\n",
      "Train Step: 554, Loss: 0.2987\n",
      "Train Step: 555, Loss: 0.4732\n",
      "Train Step: 556, Loss: 0.4072\n",
      "Train Step: 557, Loss: 0.3939\n",
      "Train Step: 558, Loss: 0.4175\n",
      "Train Step: 559, Loss: 0.4540\n",
      "Train Step: 560, Loss: 0.6555\n",
      "Train Step: 561, Loss: 0.5792\n",
      "Train Step: 562, Loss: 0.5074\n",
      "Train Step: 563, Loss: 0.4553\n",
      "Train Step: 564, Loss: 0.4437\n",
      "Train Step: 565, Loss: 0.4402\n",
      "Train Step: 566, Loss: 0.4820\n",
      "Train Step: 567, Loss: 0.3481\n",
      "Train Step: 568, Loss: 0.5429\n",
      "Train Step: 569, Loss: 0.5191\n",
      "Train Step: 570, Loss: 0.4515\n",
      "Train Step: 571, Loss: 0.4079\n",
      "Train Step: 572, Loss: 0.6313\n",
      "Train Step: 573, Loss: 0.4076\n",
      "Train Step: 574, Loss: 0.4109\n",
      "Train Step: 575, Loss: 0.4838\n",
      "Train Step: 576, Loss: 0.4709\n",
      "Train Step: 577, Loss: 0.6666\n",
      "Train Step: 578, Loss: 0.5432\n",
      "Train Step: 579, Loss: 0.4954\n",
      "Train Step: 580, Loss: 0.6501\n",
      "Train Step: 581, Loss: 0.6152\n",
      "Train Step: 582, Loss: 0.3919\n",
      "Train Step: 583, Loss: 0.5515\n",
      "Train Step: 584, Loss: 0.4660\n",
      "Train Step: 585, Loss: 0.4278\n",
      "Train Step: 586, Loss: 0.5518\n",
      "Train Step: 587, Loss: 0.4354\n",
      "Train Step: 588, Loss: 0.4842\n",
      "Train Step: 589, Loss: 0.5634\n",
      "Train Step: 590, Loss: 0.5062\n",
      "Train Step: 591, Loss: 0.4919\n",
      "Train Step: 592, Loss: 0.5712\n",
      "Train Step: 593, Loss: 0.5127\n",
      "Train Step: 594, Loss: 0.3795\n",
      "Train Step: 595, Loss: 0.4534\n",
      "Train Step: 596, Loss: 0.5327\n",
      "Train Step: 597, Loss: 0.5086\n",
      "Train Step: 598, Loss: 0.4146\n",
      "Train Step: 599, Loss: 0.6602\n",
      "Train Step: 600, Loss: 0.5392\n",
      "Train Step: 601, Loss: 0.5576\n",
      "Train Step: 602, Loss: 0.4342\n",
      "Train Step: 603, Loss: 0.4145\n",
      "Train Step: 604, Loss: 0.5340\n",
      "Train Step: 605, Loss: 0.4551\n",
      "Train Step: 606, Loss: 0.5172\n",
      "Train Step: 607, Loss: 0.4925\n",
      "Train Step: 608, Loss: 0.4643\n",
      "Train Step: 609, Loss: 0.5127\n",
      "Train Step: 610, Loss: 0.6018\n",
      "Train Step: 611, Loss: 0.4207\n",
      "Train Step: 612, Loss: 0.5498\n",
      "Train Step: 613, Loss: 0.5431\n",
      "Train Step: 614, Loss: 0.5103\n",
      "Train Step: 615, Loss: 0.4895\n",
      "Train Step: 616, Loss: 0.5050\n",
      "Train Step: 617, Loss: 0.5713\n",
      "Train Step: 618, Loss: 0.5852\n",
      "Train Step: 619, Loss: 0.3873\n",
      "Train Step: 620, Loss: 0.5514\n",
      "Train Step: 621, Loss: 0.5198\n",
      "Train Step: 622, Loss: 0.4185\n",
      "Train Step: 623, Loss: 0.6204\n",
      "Train Step: 624, Loss: 0.4742\n",
      "Train Step: 625, Loss: 0.4609\n",
      "Train Step: 626, Loss: 0.5704\n",
      "Train Step: 627, Loss: 0.5077\n",
      "Train Step: 628, Loss: 0.5939\n",
      "Train Step: 629, Loss: 0.4858\n",
      "Train Step: 630, Loss: 0.4883\n",
      "Train Step: 631, Loss: 0.4817\n",
      "Train Step: 632, Loss: 0.3387\n",
      "Train Step: 633, Loss: 0.4376\n",
      "Train Step: 634, Loss: 0.5722\n",
      "Train Step: 635, Loss: 0.5536\n",
      "Train Step: 636, Loss: 0.4827\n",
      "Train Step: 637, Loss: 0.4547\n",
      "Train Step: 638, Loss: 0.5103\n",
      "Train Step: 639, Loss: 0.4300\n",
      "Train Step: 640, Loss: 0.4364\n",
      "Train Step: 641, Loss: 0.4799\n",
      "Train Step: 642, Loss: 0.4275\n",
      "Train Step: 643, Loss: 0.4271\n",
      "Train Step: 644, Loss: 0.5167\n",
      "Train Step: 645, Loss: 0.4771\n",
      "Train Step: 646, Loss: 0.7165\n",
      "Train Step: 647, Loss: 0.5943\n",
      "Train Step: 648, Loss: 0.5035\n",
      "Train Step: 649, Loss: 0.4407\n",
      "Train Step: 650, Loss: 0.5448\n",
      "Train Step: 651, Loss: 0.4307\n",
      "Train Step: 652, Loss: 0.5828\n",
      "Train Step: 653, Loss: 0.5043\n",
      "Train Step: 654, Loss: 0.3997\n",
      "Train Step: 655, Loss: 0.4814\n",
      "Train Step: 656, Loss: 0.6002\n",
      "Train Step: 657, Loss: 0.4532\n",
      "Train Step: 658, Loss: 0.4634\n",
      "Train Step: 659, Loss: 0.5606\n",
      "Train Step: 660, Loss: 0.3244\n",
      "Train Step: 661, Loss: 0.5066\n",
      "Train Step: 662, Loss: 0.3791\n",
      "Train Step: 663, Loss: 0.5113\n",
      "Train Step: 664, Loss: 0.5874\n",
      "Train Step: 665, Loss: 0.5149\n",
      "Train Step: 666, Loss: 0.4889\n",
      "Train Step: 667, Loss: 0.3711\n",
      "Train Step: 668, Loss: 0.4544\n",
      "Train Step: 669, Loss: 0.5524\n",
      "Train Step: 670, Loss: 0.5659\n",
      "Train Step: 671, Loss: 0.4865\n",
      "Train Step: 672, Loss: 0.5182\n",
      "Train Step: 673, Loss: 0.5361\n",
      "Train Step: 674, Loss: 0.5113\n",
      "Train Step: 675, Loss: 0.5286\n",
      "Train Step: 676, Loss: 0.4186\n",
      "Train Step: 677, Loss: 0.4021\n",
      "Train Step: 678, Loss: 0.5471\n",
      "Train Step: 679, Loss: 0.4973\n",
      "Train Step: 680, Loss: 0.3425\n",
      "Train Step: 681, Loss: 0.5202\n",
      "Train Step: 682, Loss: 0.3502\n",
      "Train Step: 683, Loss: 0.4257\n",
      "Train Step: 684, Loss: 0.3512\n",
      "Train Step: 685, Loss: 0.5262\n",
      "Train Step: 686, Loss: 0.5655\n",
      "Train Step: 687, Loss: 0.5575\n",
      "Train Step: 688, Loss: 0.4566\n",
      "Train Step: 689, Loss: 0.3108\n",
      "Train Step: 690, Loss: 0.5532\n",
      "Train Step: 691, Loss: 0.4065\n",
      "Train Step: 692, Loss: 0.3875\n",
      "Train Step: 693, Loss: 0.3444\n",
      "Train Step: 694, Loss: 0.5138\n",
      "Train Step: 695, Loss: 0.4098\n",
      "Train Step: 696, Loss: 0.5180\n",
      "Train Step: 697, Loss: 0.4559\n",
      "Train Step: 698, Loss: 0.3860\n",
      "Train Step: 699, Loss: 0.4751\n",
      "Train Step: 700, Loss: 0.5624\n",
      "Train Step: 701, Loss: 0.4347\n",
      "Train Step: 702, Loss: 0.4229\n",
      "Train Step: 703, Loss: 0.5031\n",
      "Train Step: 704, Loss: 0.3876\n",
      "Train Step: 705, Loss: 0.4398\n",
      "Train Step: 706, Loss: 0.5566\n",
      "Train Step: 707, Loss: 0.5873\n",
      "Train Step: 708, Loss: 0.4850\n",
      "Train Step: 709, Loss: 0.4609\n",
      "Train Step: 710, Loss: 0.3592\n",
      "Train Step: 711, Loss: 0.4046\n",
      "Train Step: 712, Loss: 0.5031\n",
      "Train Step: 713, Loss: 0.4057\n",
      "Train Step: 714, Loss: 0.5085\n",
      "Train Step: 715, Loss: 0.5212\n",
      "Train Step: 716, Loss: 0.4311\n",
      "Train Step: 717, Loss: 0.4208\n",
      "Train Step: 718, Loss: 0.5026\n",
      "Train Step: 719, Loss: 0.5119\n",
      "Train Step: 720, Loss: 0.4764\n",
      "Train Step: 721, Loss: 0.5263\n",
      "Train Step: 722, Loss: 0.5125\n",
      "Train Step: 723, Loss: 0.3501\n",
      "Train Step: 724, Loss: 0.4563\n",
      "Train Step: 725, Loss: 0.4562\n",
      "Train Step: 726, Loss: 0.3725\n",
      "Train Step: 727, Loss: 0.6631\n",
      "Train Step: 728, Loss: 0.5292\n",
      "Train Step: 729, Loss: 0.5614\n",
      "Train Step: 730, Loss: 0.4202\n",
      "Train Step: 731, Loss: 0.3832\n",
      "Train Step: 732, Loss: 0.6321\n",
      "Train Step: 733, Loss: 0.4765\n",
      "Train Step: 734, Loss: 0.5662\n",
      "Train Step: 735, Loss: 0.3253\n",
      "Train Step: 736, Loss: 0.5547\n",
      "Train Step: 737, Loss: 0.4099\n",
      "Train Step: 738, Loss: 0.4833\n",
      "Train Step: 739, Loss: 0.3444\n",
      "Train Step: 740, Loss: 0.5309\n",
      "Train Step: 741, Loss: 0.4565\n",
      "Train Step: 742, Loss: 0.3961\n",
      "Train Step: 743, Loss: 0.4958\n",
      "Train Step: 744, Loss: 0.3589\n",
      "Train Step: 745, Loss: 0.4204\n",
      "Train Step: 746, Loss: 0.5246\n",
      "Train Step: 747, Loss: 0.5118\n",
      "Train Step: 748, Loss: 0.4753\n",
      "Train Step: 749, Loss: 0.3214\n",
      "Train Step: 750, Loss: 0.5054\n",
      "Train Step: 751, Loss: 0.5305\n",
      "Train Step: 752, Loss: 0.4978\n",
      "Train Step: 753, Loss: 0.5632\n",
      "Train Step: 754, Loss: 0.4669\n",
      "Train Step: 755, Loss: 0.4330\n",
      "Train Step: 756, Loss: 0.5621\n",
      "Train Step: 757, Loss: 0.4832\n",
      "Train Step: 758, Loss: 0.5637\n",
      "Train Step: 759, Loss: 0.5807\n",
      "Train Step: 760, Loss: 0.5088\n",
      "Train Step: 761, Loss: 0.4708\n",
      "Train Step: 762, Loss: 0.5770\n",
      "Train Step: 763, Loss: 0.6312\n",
      "Train Step: 764, Loss: 0.4341\n",
      "Train Step: 765, Loss: 0.4861\n",
      "Train Step: 766, Loss: 0.3869\n",
      "Train Step: 767, Loss: 0.5255\n",
      "Train Step: 768, Loss: 0.4555\n",
      "Train Step: 769, Loss: 0.4255\n",
      "Train Step: 770, Loss: 0.4129\n",
      "Train Step: 771, Loss: 0.4419\n",
      "Train Step: 772, Loss: 0.4255\n",
      "Train Step: 773, Loss: 0.4206\n",
      "Train Step: 774, Loss: 0.3537\n",
      "Train Step: 775, Loss: 0.5196\n",
      "Train Step: 776, Loss: 0.3938\n",
      "Train Step: 777, Loss: 0.5700\n",
      "Train Step: 778, Loss: 0.6143\n",
      "Train Step: 779, Loss: 0.3906\n",
      "Train Step: 780, Loss: 0.4680\n",
      "Train Step: 781, Loss: 0.4476\n",
      "Train Step: 782, Loss: 0.5006\n",
      "Epoch [2/10], Loss: 0.4914\n",
      "Train Step: 783, Loss: 0.4552\n",
      "Train Step: 784, Loss: 0.5356\n",
      "Train Step: 785, Loss: 0.4915\n",
      "Train Step: 786, Loss: 0.4096\n",
      "Train Step: 787, Loss: 0.4568\n",
      "Train Step: 788, Loss: 0.5024\n",
      "Train Step: 789, Loss: 0.4634\n",
      "Train Step: 790, Loss: 0.4377\n",
      "Train Step: 791, Loss: 0.4150\n",
      "Train Step: 792, Loss: 0.3001\n",
      "Train Step: 793, Loss: 0.3696\n",
      "Train Step: 794, Loss: 0.3900\n",
      "Train Step: 795, Loss: 0.3047\n",
      "Train Step: 796, Loss: 0.4206\n",
      "Train Step: 797, Loss: 0.5160\n",
      "Train Step: 798, Loss: 0.4354\n",
      "Train Step: 799, Loss: 0.3858\n",
      "Train Step: 800, Loss: 0.4585\n",
      "Train Step: 801, Loss: 0.4323\n",
      "Train Step: 802, Loss: 0.5099\n",
      "Train Step: 803, Loss: 0.3602\n",
      "Train Step: 804, Loss: 0.4848\n",
      "Train Step: 805, Loss: 0.3268\n",
      "Train Step: 806, Loss: 0.3083\n",
      "Train Step: 807, Loss: 0.3562\n",
      "Train Step: 808, Loss: 0.4078\n",
      "Train Step: 809, Loss: 0.5647\n",
      "Train Step: 810, Loss: 0.4686\n",
      "Train Step: 811, Loss: 0.4271\n",
      "Train Step: 812, Loss: 0.3913\n",
      "Train Step: 813, Loss: 0.4766\n",
      "Train Step: 814, Loss: 0.5364\n",
      "Train Step: 815, Loss: 0.5091\n",
      "Train Step: 816, Loss: 0.3991\n",
      "Train Step: 817, Loss: 0.3870\n",
      "Train Step: 818, Loss: 0.5096\n",
      "Train Step: 819, Loss: 0.4326\n",
      "Train Step: 820, Loss: 0.4255\n",
      "Train Step: 821, Loss: 0.4910\n",
      "Train Step: 822, Loss: 0.4384\n",
      "Train Step: 823, Loss: 0.4312\n",
      "Train Step: 824, Loss: 0.4050\n",
      "Train Step: 825, Loss: 0.4688\n",
      "Train Step: 826, Loss: 0.3190\n",
      "Train Step: 827, Loss: 0.4332\n",
      "Train Step: 828, Loss: 0.3976\n",
      "Train Step: 829, Loss: 0.3456\n",
      "Train Step: 830, Loss: 0.3910\n",
      "Train Step: 831, Loss: 0.3699\n",
      "Train Step: 832, Loss: 0.4465\n",
      "Train Step: 833, Loss: 0.5115\n",
      "Train Step: 834, Loss: 0.3735\n",
      "Train Step: 835, Loss: 0.3677\n",
      "Train Step: 836, Loss: 0.4328\n",
      "Train Step: 837, Loss: 0.4923\n",
      "Train Step: 838, Loss: 0.3813\n",
      "Train Step: 839, Loss: 0.5499\n",
      "Train Step: 840, Loss: 0.3729\n",
      "Train Step: 841, Loss: 0.4682\n",
      "Train Step: 842, Loss: 0.4221\n",
      "Train Step: 843, Loss: 0.3912\n",
      "Train Step: 844, Loss: 0.3294\n",
      "Train Step: 845, Loss: 0.4017\n",
      "Train Step: 846, Loss: 0.3424\n",
      "Train Step: 847, Loss: 0.3184\n",
      "Train Step: 848, Loss: 0.4923\n",
      "Train Step: 849, Loss: 0.4907\n",
      "Train Step: 850, Loss: 0.4111\n",
      "Train Step: 851, Loss: 0.6280\n",
      "Train Step: 852, Loss: 0.4043\n",
      "Train Step: 853, Loss: 0.4379\n",
      "Train Step: 854, Loss: 0.4273\n",
      "Train Step: 855, Loss: 0.6257\n",
      "Train Step: 856, Loss: 0.3974\n",
      "Train Step: 857, Loss: 0.6311\n",
      "Train Step: 858, Loss: 0.4506\n",
      "Train Step: 859, Loss: 0.2867\n",
      "Train Step: 860, Loss: 0.4219\n",
      "Train Step: 861, Loss: 0.4921\n",
      "Train Step: 862, Loss: 0.5956\n",
      "Train Step: 863, Loss: 0.4596\n",
      "Train Step: 864, Loss: 0.4356\n",
      "Train Step: 865, Loss: 0.3694\n",
      "Train Step: 866, Loss: 0.3819\n",
      "Train Step: 867, Loss: 0.4237\n",
      "Train Step: 868, Loss: 0.4079\n",
      "Train Step: 869, Loss: 0.4608\n",
      "Train Step: 870, Loss: 0.4502\n",
      "Train Step: 871, Loss: 0.3965\n",
      "Train Step: 872, Loss: 0.3291\n",
      "Train Step: 873, Loss: 0.4577\n",
      "Train Step: 874, Loss: 0.5783\n",
      "Train Step: 875, Loss: 0.4469\n",
      "Train Step: 876, Loss: 0.4651\n",
      "Train Step: 877, Loss: 0.4055\n",
      "Train Step: 878, Loss: 0.4851\n",
      "Train Step: 879, Loss: 0.4376\n",
      "Train Step: 880, Loss: 0.4357\n",
      "Train Step: 881, Loss: 0.4186\n",
      "Train Step: 882, Loss: 0.4640\n",
      "Train Step: 883, Loss: 0.4529\n",
      "Train Step: 884, Loss: 0.3935\n",
      "Train Step: 885, Loss: 0.4019\n",
      "Train Step: 886, Loss: 0.4659\n",
      "Train Step: 887, Loss: 0.4742\n",
      "Train Step: 888, Loss: 0.3670\n",
      "Train Step: 889, Loss: 0.4140\n",
      "Train Step: 890, Loss: 0.3343\n",
      "Train Step: 891, Loss: 0.6800\n",
      "Train Step: 892, Loss: 0.4201\n",
      "Train Step: 893, Loss: 0.4882\n",
      "Train Step: 894, Loss: 0.5961\n",
      "Train Step: 895, Loss: 0.3583\n",
      "Train Step: 896, Loss: 0.4508\n",
      "Train Step: 897, Loss: 0.3357\n",
      "Train Step: 898, Loss: 0.3233\n",
      "Train Step: 899, Loss: 0.3010\n",
      "Train Step: 900, Loss: 0.4263\n",
      "Train Step: 901, Loss: 0.4106\n",
      "Train Step: 902, Loss: 0.4332\n",
      "Train Step: 903, Loss: 0.5222\n",
      "Train Step: 904, Loss: 0.5292\n",
      "Train Step: 905, Loss: 0.2956\n",
      "Train Step: 906, Loss: 0.3075\n",
      "Train Step: 907, Loss: 0.4846\n",
      "Train Step: 908, Loss: 0.4531\n",
      "Train Step: 909, Loss: 0.4892\n",
      "Train Step: 910, Loss: 0.3764\n",
      "Train Step: 911, Loss: 0.5053\n",
      "Train Step: 912, Loss: 0.4475\n",
      "Train Step: 913, Loss: 0.4154\n",
      "Train Step: 914, Loss: 0.4482\n",
      "Train Step: 915, Loss: 0.3125\n",
      "Train Step: 916, Loss: 0.6021\n",
      "Train Step: 917, Loss: 0.3255\n",
      "Train Step: 918, Loss: 0.5380\n",
      "Train Step: 919, Loss: 0.4422\n",
      "Train Step: 920, Loss: 0.3887\n",
      "Train Step: 921, Loss: 0.5016\n",
      "Train Step: 922, Loss: 0.4792\n",
      "Train Step: 923, Loss: 0.4466\n",
      "Train Step: 924, Loss: 0.3632\n",
      "Train Step: 925, Loss: 0.4679\n",
      "Train Step: 926, Loss: 0.3979\n",
      "Train Step: 927, Loss: 0.4553\n",
      "Train Step: 928, Loss: 0.5507\n",
      "Train Step: 929, Loss: 0.3798\n",
      "Train Step: 930, Loss: 0.5244\n",
      "Train Step: 931, Loss: 0.6567\n",
      "Train Step: 932, Loss: 0.3325\n",
      "Train Step: 933, Loss: 0.4955\n",
      "Train Step: 934, Loss: 0.4259\n",
      "Train Step: 935, Loss: 0.3492\n",
      "Train Step: 936, Loss: 0.4020\n",
      "Train Step: 937, Loss: 0.5448\n",
      "Train Step: 938, Loss: 0.4009\n",
      "Train Step: 939, Loss: 0.4241\n",
      "Train Step: 940, Loss: 0.4096\n",
      "Train Step: 941, Loss: 0.4823\n",
      "Train Step: 942, Loss: 0.4456\n",
      "Train Step: 943, Loss: 0.4086\n",
      "Train Step: 944, Loss: 0.2958\n",
      "Train Step: 945, Loss: 0.3290\n",
      "Train Step: 946, Loss: 0.3639\n",
      "Train Step: 947, Loss: 0.3636\n",
      "Train Step: 948, Loss: 0.3977\n",
      "Train Step: 949, Loss: 0.4977\n",
      "Train Step: 950, Loss: 0.3645\n",
      "Train Step: 951, Loss: 0.4573\n",
      "Train Step: 952, Loss: 0.3747\n",
      "Train Step: 953, Loss: 0.2915\n",
      "Train Step: 954, Loss: 0.4322\n",
      "Train Step: 955, Loss: 0.5222\n",
      "Train Step: 956, Loss: 0.4125\n",
      "Train Step: 957, Loss: 0.3962\n",
      "Train Step: 958, Loss: 0.5018\n",
      "Train Step: 959, Loss: 0.3492\n",
      "Train Step: 960, Loss: 0.3971\n",
      "Train Step: 961, Loss: 0.3189\n",
      "Train Step: 962, Loss: 0.5177\n",
      "Train Step: 963, Loss: 0.3736\n",
      "Train Step: 964, Loss: 0.5546\n",
      "Train Step: 965, Loss: 0.4588\n",
      "Train Step: 966, Loss: 0.5032\n",
      "Train Step: 967, Loss: 0.4733\n",
      "Train Step: 968, Loss: 0.5124\n",
      "Train Step: 969, Loss: 0.4515\n",
      "Train Step: 970, Loss: 0.5290\n",
      "Train Step: 971, Loss: 0.3096\n",
      "Train Step: 972, Loss: 0.3883\n",
      "Train Step: 973, Loss: 0.3666\n",
      "Train Step: 974, Loss: 0.4232\n",
      "Train Step: 975, Loss: 0.4348\n",
      "Train Step: 976, Loss: 0.4123\n",
      "Train Step: 977, Loss: 0.4902\n",
      "Train Step: 978, Loss: 0.3198\n",
      "Train Step: 979, Loss: 0.2868\n",
      "Train Step: 980, Loss: 0.3963\n",
      "Train Step: 981, Loss: 0.3324\n",
      "Train Step: 982, Loss: 0.5205\n",
      "Train Step: 983, Loss: 0.4321\n",
      "Train Step: 984, Loss: 0.3624\n",
      "Train Step: 985, Loss: 0.2867\n",
      "Train Step: 986, Loss: 0.5083\n",
      "Train Step: 987, Loss: 0.4027\n",
      "Train Step: 988, Loss: 0.4538\n",
      "Train Step: 989, Loss: 0.5190\n",
      "Train Step: 990, Loss: 0.3636\n",
      "Train Step: 991, Loss: 0.4505\n",
      "Train Step: 992, Loss: 0.4637\n",
      "Train Step: 993, Loss: 0.5512\n",
      "Train Step: 994, Loss: 0.3871\n",
      "Train Step: 995, Loss: 0.4675\n",
      "Train Step: 996, Loss: 0.3376\n",
      "Train Step: 997, Loss: 0.3453\n",
      "Train Step: 998, Loss: 0.4007\n",
      "Train Step: 999, Loss: 0.5242\n",
      "Train Step: 1000, Loss: 0.3243\n",
      "Train Step: 1001, Loss: 0.3943\n",
      "Train Step: 1002, Loss: 0.3498\n",
      "Train Step: 1003, Loss: 0.3889\n",
      "Train Step: 1004, Loss: 0.5271\n",
      "Train Step: 1005, Loss: 0.2795\n",
      "Train Step: 1006, Loss: 0.4513\n",
      "Train Step: 1007, Loss: 0.4207\n",
      "Train Step: 1008, Loss: 0.3902\n",
      "Train Step: 1009, Loss: 0.3175\n",
      "Train Step: 1010, Loss: 0.4785\n",
      "Train Step: 1011, Loss: 0.4516\n",
      "Train Step: 1012, Loss: 0.5576\n",
      "Train Step: 1013, Loss: 0.4400\n",
      "Train Step: 1014, Loss: 0.3972\n",
      "Train Step: 1015, Loss: 0.3795\n",
      "Train Step: 1016, Loss: 0.3832\n",
      "Train Step: 1017, Loss: 0.3965\n",
      "Train Step: 1018, Loss: 0.4667\n",
      "Train Step: 1019, Loss: 0.4046\n",
      "Train Step: 1020, Loss: 0.4027\n",
      "Train Step: 1021, Loss: 0.3630\n",
      "Train Step: 1022, Loss: 0.5724\n",
      "Train Step: 1023, Loss: 0.5681\n",
      "Train Step: 1024, Loss: 0.3755\n",
      "Train Step: 1025, Loss: 0.5418\n",
      "Train Step: 1026, Loss: 0.3980\n",
      "Train Step: 1027, Loss: 0.6283\n",
      "Train Step: 1028, Loss: 0.3717\n",
      "Train Step: 1029, Loss: 0.2610\n",
      "Train Step: 1030, Loss: 0.3888\n",
      "Train Step: 1031, Loss: 0.4007\n",
      "Train Step: 1032, Loss: 0.4730\n",
      "Train Step: 1033, Loss: 0.3995\n",
      "Train Step: 1034, Loss: 0.3899\n",
      "Train Step: 1035, Loss: 0.3681\n",
      "Train Step: 1036, Loss: 0.3195\n",
      "Train Step: 1037, Loss: 0.3529\n",
      "Train Step: 1038, Loss: 0.5500\n",
      "Train Step: 1039, Loss: 0.3249\n",
      "Train Step: 1040, Loss: 0.3473\n",
      "Train Step: 1041, Loss: 0.3972\n",
      "Train Step: 1042, Loss: 0.4886\n",
      "Train Step: 1043, Loss: 0.4732\n",
      "Train Step: 1044, Loss: 0.4685\n",
      "Train Step: 1045, Loss: 0.4277\n",
      "Train Step: 1046, Loss: 0.3272\n",
      "Train Step: 1047, Loss: 0.2974\n",
      "Train Step: 1048, Loss: 0.3158\n",
      "Train Step: 1049, Loss: 0.3541\n",
      "Train Step: 1050, Loss: 0.3584\n",
      "Train Step: 1051, Loss: 0.4117\n",
      "Train Step: 1052, Loss: 0.3829\n",
      "Train Step: 1053, Loss: 0.3282\n",
      "Train Step: 1054, Loss: 0.3833\n",
      "Train Step: 1055, Loss: 0.4128\n",
      "Train Step: 1056, Loss: 0.4441\n",
      "Train Step: 1057, Loss: 0.5811\n",
      "Train Step: 1058, Loss: 0.5139\n",
      "Train Step: 1059, Loss: 0.3335\n",
      "Train Step: 1060, Loss: 0.5265\n",
      "Train Step: 1061, Loss: 0.3409\n",
      "Train Step: 1062, Loss: 0.5152\n",
      "Train Step: 1063, Loss: 0.3848\n",
      "Train Step: 1064, Loss: 0.4383\n",
      "Train Step: 1065, Loss: 0.4003\n",
      "Train Step: 1066, Loss: 0.5586\n",
      "Train Step: 1067, Loss: 0.3897\n",
      "Train Step: 1068, Loss: 0.4612\n",
      "Train Step: 1069, Loss: 0.3853\n",
      "Train Step: 1070, Loss: 0.4284\n",
      "Train Step: 1071, Loss: 0.3836\n",
      "Train Step: 1072, Loss: 0.4158\n",
      "Train Step: 1073, Loss: 0.5561\n",
      "Train Step: 1074, Loss: 0.3536\n",
      "Train Step: 1075, Loss: 0.4070\n",
      "Train Step: 1076, Loss: 0.4304\n",
      "Train Step: 1077, Loss: 0.3415\n",
      "Train Step: 1078, Loss: 0.4661\n",
      "Train Step: 1079, Loss: 0.3393\n",
      "Train Step: 1080, Loss: 0.3287\n",
      "Train Step: 1081, Loss: 0.4934\n",
      "Train Step: 1082, Loss: 0.4565\n",
      "Train Step: 1083, Loss: 0.4370\n",
      "Train Step: 1084, Loss: 0.5157\n",
      "Train Step: 1085, Loss: 0.3787\n",
      "Train Step: 1086, Loss: 0.5030\n",
      "Train Step: 1087, Loss: 0.4175\n",
      "Train Step: 1088, Loss: 0.4245\n",
      "Train Step: 1089, Loss: 0.4622\n",
      "Train Step: 1090, Loss: 0.4710\n",
      "Train Step: 1091, Loss: 0.5961\n",
      "Train Step: 1092, Loss: 0.4186\n",
      "Train Step: 1093, Loss: 0.5033\n",
      "Train Step: 1094, Loss: 0.3487\n",
      "Train Step: 1095, Loss: 0.4552\n",
      "Train Step: 1096, Loss: 0.3545\n",
      "Train Step: 1097, Loss: 0.3752\n",
      "Train Step: 1098, Loss: 0.3255\n",
      "Train Step: 1099, Loss: 0.3490\n",
      "Train Step: 1100, Loss: 0.5254\n",
      "Train Step: 1101, Loss: 0.4892\n",
      "Train Step: 1102, Loss: 0.4100\n",
      "Train Step: 1103, Loss: 0.3686\n",
      "Train Step: 1104, Loss: 0.4147\n",
      "Train Step: 1105, Loss: 0.3583\n",
      "Train Step: 1106, Loss: 0.3318\n",
      "Train Step: 1107, Loss: 0.4265\n",
      "Train Step: 1108, Loss: 0.3560\n",
      "Train Step: 1109, Loss: 0.4641\n",
      "Train Step: 1110, Loss: 0.3479\n",
      "Train Step: 1111, Loss: 0.4667\n",
      "Train Step: 1112, Loss: 0.4257\n",
      "Train Step: 1113, Loss: 0.2953\n",
      "Train Step: 1114, Loss: 0.4195\n",
      "Train Step: 1115, Loss: 0.3724\n",
      "Train Step: 1116, Loss: 0.6035\n",
      "Train Step: 1117, Loss: 0.3688\n",
      "Train Step: 1118, Loss: 0.3981\n",
      "Train Step: 1119, Loss: 0.3293\n",
      "Train Step: 1120, Loss: 0.3121\n",
      "Train Step: 1121, Loss: 0.3413\n",
      "Train Step: 1122, Loss: 0.5072\n",
      "Train Step: 1123, Loss: 0.2961\n",
      "Train Step: 1124, Loss: 0.4528\n",
      "Train Step: 1125, Loss: 0.4833\n",
      "Train Step: 1126, Loss: 0.3320\n",
      "Train Step: 1127, Loss: 0.3714\n",
      "Train Step: 1128, Loss: 0.3892\n",
      "Train Step: 1129, Loss: 0.3157\n",
      "Train Step: 1130, Loss: 0.3509\n",
      "Train Step: 1131, Loss: 0.4727\n",
      "Train Step: 1132, Loss: 0.2807\n",
      "Train Step: 1133, Loss: 0.4160\n",
      "Train Step: 1134, Loss: 0.4989\n",
      "Train Step: 1135, Loss: 0.3962\n",
      "Train Step: 1136, Loss: 0.3668\n",
      "Train Step: 1137, Loss: 0.3755\n",
      "Train Step: 1138, Loss: 0.4063\n",
      "Train Step: 1139, Loss: 0.3990\n",
      "Train Step: 1140, Loss: 0.4273\n",
      "Train Step: 1141, Loss: 0.4576\n",
      "Train Step: 1142, Loss: 0.4135\n",
      "Train Step: 1143, Loss: 0.2880\n",
      "Train Step: 1144, Loss: 0.3262\n",
      "Train Step: 1145, Loss: 0.4360\n",
      "Train Step: 1146, Loss: 0.3811\n",
      "Train Step: 1147, Loss: 0.3437\n",
      "Train Step: 1148, Loss: 0.3005\n",
      "Train Step: 1149, Loss: 0.5125\n",
      "Train Step: 1150, Loss: 0.4832\n",
      "Train Step: 1151, Loss: 0.3594\n",
      "Train Step: 1152, Loss: 0.4200\n",
      "Train Step: 1153, Loss: 0.4026\n",
      "Train Step: 1154, Loss: 0.5833\n",
      "Train Step: 1155, Loss: 0.4477\n",
      "Train Step: 1156, Loss: 0.3547\n",
      "Train Step: 1157, Loss: 0.3839\n",
      "Train Step: 1158, Loss: 0.5333\n",
      "Train Step: 1159, Loss: 0.4504\n",
      "Train Step: 1160, Loss: 0.3672\n",
      "Train Step: 1161, Loss: 0.4283\n",
      "Train Step: 1162, Loss: 0.3896\n",
      "Train Step: 1163, Loss: 0.3893\n",
      "Train Step: 1164, Loss: 0.4945\n",
      "Train Step: 1165, Loss: 0.4113\n",
      "Train Step: 1166, Loss: 0.3938\n",
      "Train Step: 1167, Loss: 0.4275\n",
      "Train Step: 1168, Loss: 0.5519\n",
      "Train Step: 1169, Loss: 0.5495\n",
      "Train Step: 1170, Loss: 0.2841\n",
      "Train Step: 1171, Loss: 0.3081\n",
      "Train Step: 1172, Loss: 0.4642\n",
      "Train Step: 1173, Loss: 0.3044\n",
      "Epoch [3/10], Loss: 0.4231\n",
      "Train Step: 1174, Loss: 0.2334\n",
      "Train Step: 1175, Loss: 0.3882\n",
      "Train Step: 1176, Loss: 0.2662\n",
      "Train Step: 1177, Loss: 0.3711\n",
      "Train Step: 1178, Loss: 0.4115\n",
      "Train Step: 1179, Loss: 0.4550\n",
      "Train Step: 1180, Loss: 0.3921\n",
      "Train Step: 1181, Loss: 0.3549\n",
      "Train Step: 1182, Loss: 0.4055\n",
      "Train Step: 1183, Loss: 0.2190\n",
      "Train Step: 1184, Loss: 0.3054\n",
      "Train Step: 1185, Loss: 0.2802\n",
      "Train Step: 1186, Loss: 0.3808\n",
      "Train Step: 1187, Loss: 0.2948\n",
      "Train Step: 1188, Loss: 0.3922\n",
      "Train Step: 1189, Loss: 0.3306\n",
      "Train Step: 1190, Loss: 0.2833\n",
      "Train Step: 1191, Loss: 0.3546\n",
      "Train Step: 1192, Loss: 0.3222\n",
      "Train Step: 1193, Loss: 0.6327\n",
      "Train Step: 1194, Loss: 0.3582\n",
      "Train Step: 1195, Loss: 0.4820\n",
      "Train Step: 1196, Loss: 0.4507\n",
      "Train Step: 1197, Loss: 0.4137\n",
      "Train Step: 1198, Loss: 0.3349\n",
      "Train Step: 1199, Loss: 0.2555\n",
      "Train Step: 1200, Loss: 0.4817\n",
      "Train Step: 1201, Loss: 0.4960\n",
      "Train Step: 1202, Loss: 0.3370\n",
      "Train Step: 1203, Loss: 0.3324\n",
      "Train Step: 1204, Loss: 0.3594\n",
      "Train Step: 1205, Loss: 0.3803\n",
      "Train Step: 1206, Loss: 0.3851\n",
      "Train Step: 1207, Loss: 0.3625\n",
      "Train Step: 1208, Loss: 0.4130\n",
      "Train Step: 1209, Loss: 0.5053\n",
      "Train Step: 1210, Loss: 0.3480\n",
      "Train Step: 1211, Loss: 0.2622\n",
      "Train Step: 1212, Loss: 0.2516\n",
      "Train Step: 1213, Loss: 0.3815\n",
      "Train Step: 1214, Loss: 0.4262\n",
      "Train Step: 1215, Loss: 0.3443\n",
      "Train Step: 1216, Loss: 0.3522\n",
      "Train Step: 1217, Loss: 0.2723\n",
      "Train Step: 1218, Loss: 0.3438\n",
      "Train Step: 1219, Loss: 0.5260\n",
      "Train Step: 1220, Loss: 0.3846\n",
      "Train Step: 1221, Loss: 0.3742\n",
      "Train Step: 1222, Loss: 0.3109\n",
      "Train Step: 1223, Loss: 0.3190\n",
      "Train Step: 1224, Loss: 0.3047\n",
      "Train Step: 1225, Loss: 0.3653\n",
      "Train Step: 1226, Loss: 0.4805\n",
      "Train Step: 1227, Loss: 0.3598\n",
      "Train Step: 1228, Loss: 0.5087\n",
      "Train Step: 1229, Loss: 0.3869\n",
      "Train Step: 1230, Loss: 0.4145\n",
      "Train Step: 1231, Loss: 0.2581\n",
      "Train Step: 1232, Loss: 0.4245\n",
      "Train Step: 1233, Loss: 0.3363\n",
      "Train Step: 1234, Loss: 0.3038\n",
      "Train Step: 1235, Loss: 0.2501\n",
      "Train Step: 1236, Loss: 0.3600\n",
      "Train Step: 1237, Loss: 0.4059\n",
      "Train Step: 1238, Loss: 0.4361\n",
      "Train Step: 1239, Loss: 0.5551\n",
      "Train Step: 1240, Loss: 0.2435\n",
      "Train Step: 1241, Loss: 0.4783\n",
      "Train Step: 1242, Loss: 0.4067\n",
      "Train Step: 1243, Loss: 0.3068\n",
      "Train Step: 1244, Loss: 0.4306\n",
      "Train Step: 1245, Loss: 0.3997\n",
      "Train Step: 1246, Loss: 0.3338\n",
      "Train Step: 1247, Loss: 0.4553\n",
      "Train Step: 1248, Loss: 0.3892\n",
      "Train Step: 1249, Loss: 0.3342\n",
      "Train Step: 1250, Loss: 0.4408\n",
      "Train Step: 1251, Loss: 0.3774\n",
      "Train Step: 1252, Loss: 0.4243\n",
      "Train Step: 1253, Loss: 0.2771\n",
      "Train Step: 1254, Loss: 0.3911\n",
      "Train Step: 1255, Loss: 0.4327\n",
      "Train Step: 1256, Loss: 0.3599\n",
      "Train Step: 1257, Loss: 0.3726\n",
      "Train Step: 1258, Loss: 0.3508\n",
      "Train Step: 1259, Loss: 0.4692\n",
      "Train Step: 1260, Loss: 0.3719\n",
      "Train Step: 1261, Loss: 0.4905\n",
      "Train Step: 1262, Loss: 0.3063\n",
      "Train Step: 1263, Loss: 0.4736\n",
      "Train Step: 1264, Loss: 0.4359\n",
      "Train Step: 1265, Loss: 0.3793\n",
      "Train Step: 1266, Loss: 0.4468\n",
      "Train Step: 1267, Loss: 0.4485\n",
      "Train Step: 1268, Loss: 0.3076\n",
      "Train Step: 1269, Loss: 0.3171\n",
      "Train Step: 1270, Loss: 0.4726\n",
      "Train Step: 1271, Loss: 0.3302\n",
      "Train Step: 1272, Loss: 0.3991\n",
      "Train Step: 1273, Loss: 0.2635\n",
      "Train Step: 1274, Loss: 0.2492\n",
      "Train Step: 1275, Loss: 0.4227\n",
      "Train Step: 1276, Loss: 0.5655\n",
      "Train Step: 1277, Loss: 0.3193\n",
      "Train Step: 1278, Loss: 0.5269\n",
      "Train Step: 1279, Loss: 0.3051\n",
      "Train Step: 1280, Loss: 0.3243\n",
      "Train Step: 1281, Loss: 0.4053\n",
      "Train Step: 1282, Loss: 0.5018\n",
      "Train Step: 1283, Loss: 0.3148\n",
      "Train Step: 1284, Loss: 0.4603\n",
      "Train Step: 1285, Loss: 0.3515\n",
      "Train Step: 1286, Loss: 0.3503\n",
      "Train Step: 1287, Loss: 0.3657\n",
      "Train Step: 1288, Loss: 0.3991\n",
      "Train Step: 1289, Loss: 0.4443\n",
      "Train Step: 1290, Loss: 0.4059\n",
      "Train Step: 1291, Loss: 0.2697\n",
      "Train Step: 1292, Loss: 0.3720\n",
      "Train Step: 1293, Loss: 0.4049\n",
      "Train Step: 1294, Loss: 0.3037\n",
      "Train Step: 1295, Loss: 0.4348\n",
      "Train Step: 1296, Loss: 0.2720\n",
      "Train Step: 1297, Loss: 0.2207\n",
      "Train Step: 1298, Loss: 0.3854\n",
      "Train Step: 1299, Loss: 0.2785\n",
      "Train Step: 1300, Loss: 0.3514\n",
      "Train Step: 1301, Loss: 0.3175\n",
      "Train Step: 1302, Loss: 0.2260\n",
      "Train Step: 1303, Loss: 0.4431\n",
      "Train Step: 1304, Loss: 0.4833\n",
      "Train Step: 1305, Loss: 0.4131\n",
      "Train Step: 1306, Loss: 0.4515\n",
      "Train Step: 1307, Loss: 0.2999\n",
      "Train Step: 1308, Loss: 0.4151\n",
      "Train Step: 1309, Loss: 0.2547\n",
      "Train Step: 1310, Loss: 0.3290\n",
      "Train Step: 1311, Loss: 0.2072\n",
      "Train Step: 1312, Loss: 0.2735\n",
      "Train Step: 1313, Loss: 0.2800\n",
      "Train Step: 1314, Loss: 0.4033\n",
      "Train Step: 1315, Loss: 0.3287\n",
      "Train Step: 1316, Loss: 0.3794\n",
      "Train Step: 1317, Loss: 0.3828\n",
      "Train Step: 1318, Loss: 0.4213\n",
      "Train Step: 1319, Loss: 0.3195\n",
      "Train Step: 1320, Loss: 0.3076\n",
      "Train Step: 1321, Loss: 0.4231\n",
      "Train Step: 1322, Loss: 0.3318\n",
      "Train Step: 1323, Loss: 0.3247\n",
      "Train Step: 1324, Loss: 0.3774\n",
      "Train Step: 1325, Loss: 0.3741\n",
      "Train Step: 1326, Loss: 0.4029\n",
      "Train Step: 1327, Loss: 0.3975\n",
      "Train Step: 1328, Loss: 0.4106\n",
      "Train Step: 1329, Loss: 0.4077\n",
      "Train Step: 1330, Loss: 0.3324\n",
      "Train Step: 1331, Loss: 0.3114\n",
      "Train Step: 1332, Loss: 0.4346\n",
      "Train Step: 1333, Loss: 0.2519\n",
      "Train Step: 1334, Loss: 0.4554\n",
      "Train Step: 1335, Loss: 0.3877\n",
      "Train Step: 1336, Loss: 0.3883\n",
      "Train Step: 1337, Loss: 0.2665\n",
      "Train Step: 1338, Loss: 0.3711\n",
      "Train Step: 1339, Loss: 0.3204\n",
      "Train Step: 1340, Loss: 0.2727\n",
      "Train Step: 1341, Loss: 0.3504\n",
      "Train Step: 1342, Loss: 0.3642\n",
      "Train Step: 1343, Loss: 0.3748\n",
      "Train Step: 1344, Loss: 0.4108\n",
      "Train Step: 1345, Loss: 0.3176\n",
      "Train Step: 1346, Loss: 0.2977\n",
      "Train Step: 1347, Loss: 0.4880\n",
      "Train Step: 1348, Loss: 0.3331\n",
      "Train Step: 1349, Loss: 0.3616\n",
      "Train Step: 1350, Loss: 0.3252\n",
      "Train Step: 1351, Loss: 0.3668\n",
      "Train Step: 1352, Loss: 0.3407\n",
      "Train Step: 1353, Loss: 0.2591\n",
      "Train Step: 1354, Loss: 0.3461\n",
      "Train Step: 1355, Loss: 0.2460\n",
      "Train Step: 1356, Loss: 0.4689\n",
      "Train Step: 1357, Loss: 0.2273\n",
      "Train Step: 1358, Loss: 0.2608\n",
      "Train Step: 1359, Loss: 0.3815\n",
      "Train Step: 1360, Loss: 0.4365\n",
      "Train Step: 1361, Loss: 0.3926\n",
      "Train Step: 1362, Loss: 0.2285\n",
      "Train Step: 1363, Loss: 0.5049\n",
      "Train Step: 1364, Loss: 0.2605\n",
      "Train Step: 1365, Loss: 0.2753\n",
      "Train Step: 1366, Loss: 0.4635\n",
      "Train Step: 1367, Loss: 0.4142\n",
      "Train Step: 1368, Loss: 0.2725\n",
      "Train Step: 1369, Loss: 0.3933\n",
      "Train Step: 1370, Loss: 0.3794\n",
      "Train Step: 1371, Loss: 0.4158\n",
      "Train Step: 1372, Loss: 0.3254\n",
      "Train Step: 1373, Loss: 0.3194\n",
      "Train Step: 1374, Loss: 0.3167\n",
      "Train Step: 1375, Loss: 0.2723\n",
      "Train Step: 1376, Loss: 0.3782\n",
      "Train Step: 1377, Loss: 0.2870\n",
      "Train Step: 1378, Loss: 0.3654\n",
      "Train Step: 1379, Loss: 0.3518\n",
      "Train Step: 1380, Loss: 0.5276\n",
      "Train Step: 1381, Loss: 0.2565\n",
      "Train Step: 1382, Loss: 0.2810\n",
      "Train Step: 1383, Loss: 0.5205\n",
      "Train Step: 1384, Loss: 0.3414\n",
      "Train Step: 1385, Loss: 0.3637\n",
      "Train Step: 1386, Loss: 0.3007\n",
      "Train Step: 1387, Loss: 0.5315\n",
      "Train Step: 1388, Loss: 0.3272\n",
      "Train Step: 1389, Loss: 0.3416\n",
      "Train Step: 1390, Loss: 0.3664\n",
      "Train Step: 1391, Loss: 0.2874\n",
      "Train Step: 1392, Loss: 0.3599\n",
      "Train Step: 1393, Loss: 0.3542\n",
      "Train Step: 1394, Loss: 0.2933\n",
      "Train Step: 1395, Loss: 0.4045\n",
      "Train Step: 1396, Loss: 0.3319\n",
      "Train Step: 1397, Loss: 0.4287\n",
      "Train Step: 1398, Loss: 0.3509\n",
      "Train Step: 1399, Loss: 0.4442\n",
      "Train Step: 1400, Loss: 0.2281\n",
      "Train Step: 1401, Loss: 0.2589\n",
      "Train Step: 1402, Loss: 0.3226\n",
      "Train Step: 1403, Loss: 0.3878\n",
      "Train Step: 1404, Loss: 0.4154\n",
      "Train Step: 1405, Loss: 0.4316\n",
      "Train Step: 1406, Loss: 0.4548\n",
      "Train Step: 1407, Loss: 0.3781\n",
      "Train Step: 1408, Loss: 0.2693\n",
      "Train Step: 1409, Loss: 0.2881\n",
      "Train Step: 1410, Loss: 0.4123\n",
      "Train Step: 1411, Loss: 0.3834\n",
      "Train Step: 1412, Loss: 0.3780\n",
      "Train Step: 1413, Loss: 0.4628\n",
      "Train Step: 1414, Loss: 0.2636\n",
      "Train Step: 1415, Loss: 0.3894\n",
      "Train Step: 1416, Loss: 0.3540\n",
      "Train Step: 1417, Loss: 0.2698\n",
      "Train Step: 1418, Loss: 0.4348\n",
      "Train Step: 1419, Loss: 0.3344\n",
      "Train Step: 1420, Loss: 0.4126\n",
      "Train Step: 1421, Loss: 0.4623\n",
      "Train Step: 1422, Loss: 0.4416\n",
      "Train Step: 1423, Loss: 0.3441\n",
      "Train Step: 1424, Loss: 0.3184\n",
      "Train Step: 1425, Loss: 0.3046\n",
      "Train Step: 1426, Loss: 0.3163\n",
      "Train Step: 1427, Loss: 0.3637\n",
      "Train Step: 1428, Loss: 0.3897\n",
      "Train Step: 1429, Loss: 0.3781\n",
      "Train Step: 1430, Loss: 0.3477\n",
      "Train Step: 1431, Loss: 0.3729\n",
      "Train Step: 1432, Loss: 0.2774\n",
      "Train Step: 1433, Loss: 0.4360\n",
      "Train Step: 1434, Loss: 0.3850\n",
      "Train Step: 1435, Loss: 0.3944\n",
      "Train Step: 1436, Loss: 0.4301\n",
      "Train Step: 1437, Loss: 0.3594\n",
      "Train Step: 1438, Loss: 0.2480\n",
      "Train Step: 1439, Loss: 0.4135\n",
      "Train Step: 1440, Loss: 0.3514\n",
      "Train Step: 1441, Loss: 0.3769\n",
      "Train Step: 1442, Loss: 0.2810\n",
      "Train Step: 1443, Loss: 0.3274\n",
      "Train Step: 1444, Loss: 0.2754\n",
      "Train Step: 1445, Loss: 0.3317\n",
      "Train Step: 1446, Loss: 0.3691\n",
      "Train Step: 1447, Loss: 0.2791\n",
      "Train Step: 1448, Loss: 0.2233\n",
      "Train Step: 1449, Loss: 0.3434\n",
      "Train Step: 1450, Loss: 0.4535\n",
      "Train Step: 1451, Loss: 0.3494\n",
      "Train Step: 1452, Loss: 0.3001\n",
      "Train Step: 1453, Loss: 0.3339\n",
      "Train Step: 1454, Loss: 0.2847\n",
      "Train Step: 1455, Loss: 0.5487\n",
      "Train Step: 1456, Loss: 0.4384\n",
      "Train Step: 1457, Loss: 0.3932\n",
      "Train Step: 1458, Loss: 0.4504\n",
      "Train Step: 1459, Loss: 0.2427\n",
      "Train Step: 1460, Loss: 0.3112\n",
      "Train Step: 1461, Loss: 0.3462\n",
      "Train Step: 1462, Loss: 0.4266\n",
      "Train Step: 1463, Loss: 0.3766\n",
      "Train Step: 1464, Loss: 0.2880\n",
      "Train Step: 1465, Loss: 0.4160\n",
      "Train Step: 1466, Loss: 0.3585\n",
      "Train Step: 1467, Loss: 0.3123\n",
      "Train Step: 1468, Loss: 0.3202\n",
      "Train Step: 1469, Loss: 0.3733\n",
      "Train Step: 1470, Loss: 0.2681\n",
      "Train Step: 1471, Loss: 0.2178\n",
      "Train Step: 1472, Loss: 0.3759\n",
      "Train Step: 1473, Loss: 0.3121\n",
      "Train Step: 1474, Loss: 0.3329\n",
      "Train Step: 1475, Loss: 0.3569\n",
      "Train Step: 1476, Loss: 0.3334\n",
      "Train Step: 1477, Loss: 0.3544\n",
      "Train Step: 1478, Loss: 0.3828\n",
      "Train Step: 1479, Loss: 0.3289\n",
      "Train Step: 1480, Loss: 0.4201\n",
      "Train Step: 1481, Loss: 0.3393\n",
      "Train Step: 1482, Loss: 0.4179\n",
      "Train Step: 1483, Loss: 0.3381\n",
      "Train Step: 1484, Loss: 0.2968\n",
      "Train Step: 1485, Loss: 0.5250\n",
      "Train Step: 1486, Loss: 0.2981\n",
      "Train Step: 1487, Loss: 0.4951\n",
      "Train Step: 1488, Loss: 0.3828\n",
      "Train Step: 1489, Loss: 0.2577\n",
      "Train Step: 1490, Loss: 0.5366\n",
      "Train Step: 1491, Loss: 0.2694\n",
      "Train Step: 1492, Loss: 0.2953\n",
      "Train Step: 1493, Loss: 0.3117\n",
      "Train Step: 1494, Loss: 0.3937\n",
      "Train Step: 1495, Loss: 0.3610\n",
      "Train Step: 1496, Loss: 0.2703\n",
      "Train Step: 1497, Loss: 0.4002\n",
      "Train Step: 1498, Loss: 0.3919\n",
      "Train Step: 1499, Loss: 0.2552\n",
      "Train Step: 1500, Loss: 0.1871\n",
      "Train Step: 1501, Loss: 0.3562\n",
      "Train Step: 1502, Loss: 0.2426\n",
      "Train Step: 1503, Loss: 0.3767\n",
      "Train Step: 1504, Loss: 0.3918\n",
      "Train Step: 1505, Loss: 0.2388\n",
      "Train Step: 1506, Loss: 0.2443\n",
      "Train Step: 1507, Loss: 0.2053\n",
      "Train Step: 1508, Loss: 0.2795\n",
      "Train Step: 1509, Loss: 0.2562\n",
      "Train Step: 1510, Loss: 0.3862\n",
      "Train Step: 1511, Loss: 0.3328\n",
      "Train Step: 1512, Loss: 0.3023\n",
      "Train Step: 1513, Loss: 0.3916\n",
      "Train Step: 1514, Loss: 0.2808\n",
      "Train Step: 1515, Loss: 0.4271\n",
      "Train Step: 1516, Loss: 0.3026\n",
      "Train Step: 1517, Loss: 0.2904\n",
      "Train Step: 1518, Loss: 0.2815\n",
      "Train Step: 1519, Loss: 0.4715\n",
      "Train Step: 1520, Loss: 0.4004\n",
      "Train Step: 1521, Loss: 0.3233\n",
      "Train Step: 1522, Loss: 0.3609\n",
      "Train Step: 1523, Loss: 0.3798\n",
      "Train Step: 1524, Loss: 0.3781\n",
      "Train Step: 1525, Loss: 0.3728\n",
      "Train Step: 1526, Loss: 0.3293\n",
      "Train Step: 1527, Loss: 0.4463\n",
      "Train Step: 1528, Loss: 0.3756\n",
      "Train Step: 1529, Loss: 0.3804\n",
      "Train Step: 1530, Loss: 0.2906\n",
      "Train Step: 1531, Loss: 0.4058\n",
      "Train Step: 1532, Loss: 0.3371\n",
      "Train Step: 1533, Loss: 0.3897\n",
      "Train Step: 1534, Loss: 0.3834\n",
      "Train Step: 1535, Loss: 0.2178\n",
      "Train Step: 1536, Loss: 0.2847\n",
      "Train Step: 1537, Loss: 0.3583\n",
      "Train Step: 1538, Loss: 0.4875\n",
      "Train Step: 1539, Loss: 0.4867\n",
      "Train Step: 1540, Loss: 0.2956\n",
      "Train Step: 1541, Loss: 0.2794\n",
      "Train Step: 1542, Loss: 0.4268\n",
      "Train Step: 1543, Loss: 0.4743\n",
      "Train Step: 1544, Loss: 0.3488\n",
      "Train Step: 1545, Loss: 0.3813\n",
      "Train Step: 1546, Loss: 0.3998\n",
      "Train Step: 1547, Loss: 0.4021\n",
      "Train Step: 1548, Loss: 0.3417\n",
      "Train Step: 1549, Loss: 0.4737\n",
      "Train Step: 1550, Loss: 0.2519\n",
      "Train Step: 1551, Loss: 0.3767\n",
      "Train Step: 1552, Loss: 0.4077\n",
      "Train Step: 1553, Loss: 0.3386\n",
      "Train Step: 1554, Loss: 0.4806\n",
      "Train Step: 1555, Loss: 0.3284\n",
      "Train Step: 1556, Loss: 0.4383\n",
      "Train Step: 1557, Loss: 0.2851\n",
      "Train Step: 1558, Loss: 0.3237\n",
      "Train Step: 1559, Loss: 0.2370\n",
      "Train Step: 1560, Loss: 0.4127\n",
      "Train Step: 1561, Loss: 0.3380\n",
      "Train Step: 1562, Loss: 0.2712\n",
      "Train Step: 1563, Loss: 0.3845\n",
      "Train Step: 1564, Loss: 0.4900\n",
      "Epoch [4/10], Loss: 0.3609\n",
      "Train Step: 1565, Loss: 0.3805\n",
      "Train Step: 1566, Loss: 0.2996\n",
      "Train Step: 1567, Loss: 0.2087\n",
      "Train Step: 1568, Loss: 0.2596\n",
      "Train Step: 1569, Loss: 0.4348\n",
      "Train Step: 1570, Loss: 0.2655\n",
      "Train Step: 1571, Loss: 0.2156\n",
      "Train Step: 1572, Loss: 0.2279\n",
      "Train Step: 1573, Loss: 0.2282\n",
      "Train Step: 1574, Loss: 0.3095\n",
      "Train Step: 1575, Loss: 0.2663\n",
      "Train Step: 1576, Loss: 0.3379\n",
      "Train Step: 1577, Loss: 0.2388\n",
      "Train Step: 1578, Loss: 0.2424\n",
      "Train Step: 1579, Loss: 0.3568\n",
      "Train Step: 1580, Loss: 0.2294\n",
      "Train Step: 1581, Loss: 0.3025\n",
      "Train Step: 1582, Loss: 0.2556\n",
      "Train Step: 1583, Loss: 0.2288\n",
      "Train Step: 1584, Loss: 0.3397\n",
      "Train Step: 1585, Loss: 0.2459\n",
      "Train Step: 1586, Loss: 0.2648\n",
      "Train Step: 1587, Loss: 0.2683\n",
      "Train Step: 1588, Loss: 0.2468\n",
      "Train Step: 1589, Loss: 0.2705\n",
      "Train Step: 1590, Loss: 0.3383\n",
      "Train Step: 1591, Loss: 0.2620\n",
      "Train Step: 1592, Loss: 0.2339\n",
      "Train Step: 1593, Loss: 0.3081\n",
      "Train Step: 1594, Loss: 0.2547\n",
      "Train Step: 1595, Loss: 0.2080\n",
      "Train Step: 1596, Loss: 0.2708\n",
      "Train Step: 1597, Loss: 0.3824\n",
      "Train Step: 1598, Loss: 0.2492\n",
      "Train Step: 1599, Loss: 0.3284\n",
      "Train Step: 1600, Loss: 0.3317\n",
      "Train Step: 1601, Loss: 0.2308\n",
      "Train Step: 1602, Loss: 0.2524\n",
      "Train Step: 1603, Loss: 0.4901\n",
      "Train Step: 1604, Loss: 0.2967\n",
      "Train Step: 1605, Loss: 0.2935\n",
      "Train Step: 1606, Loss: 0.3452\n",
      "Train Step: 1607, Loss: 0.2296\n",
      "Train Step: 1608, Loss: 0.3906\n",
      "Train Step: 1609, Loss: 0.2184\n",
      "Train Step: 1610, Loss: 0.5531\n",
      "Train Step: 1611, Loss: 0.3328\n",
      "Train Step: 1612, Loss: 0.3051\n",
      "Train Step: 1613, Loss: 0.2380\n",
      "Train Step: 1614, Loss: 0.2797\n",
      "Train Step: 1615, Loss: 0.2893\n",
      "Train Step: 1616, Loss: 0.2794\n",
      "Train Step: 1617, Loss: 0.2185\n",
      "Train Step: 1618, Loss: 0.3291\n",
      "Train Step: 1619, Loss: 0.2821\n",
      "Train Step: 1620, Loss: 0.1281\n",
      "Train Step: 1621, Loss: 0.2408\n",
      "Train Step: 1622, Loss: 0.2523\n",
      "Train Step: 1623, Loss: 0.3569\n",
      "Train Step: 1624, Loss: 0.2543\n",
      "Train Step: 1625, Loss: 0.1481\n",
      "Train Step: 1626, Loss: 0.3197\n",
      "Train Step: 1627, Loss: 0.3473\n",
      "Train Step: 1628, Loss: 0.2599\n",
      "Train Step: 1629, Loss: 0.3266\n",
      "Train Step: 1630, Loss: 0.4206\n",
      "Train Step: 1631, Loss: 0.3424\n",
      "Train Step: 1632, Loss: 0.2787\n",
      "Train Step: 1633, Loss: 0.4616\n",
      "Train Step: 1634, Loss: 0.1780\n",
      "Train Step: 1635, Loss: 0.3632\n",
      "Train Step: 1636, Loss: 0.4112\n",
      "Train Step: 1637, Loss: 0.2673\n",
      "Train Step: 1638, Loss: 0.1829\n",
      "Train Step: 1639, Loss: 0.3816\n",
      "Train Step: 1640, Loss: 0.2960\n",
      "Train Step: 1641, Loss: 0.2879\n",
      "Train Step: 1642, Loss: 0.3845\n",
      "Train Step: 1643, Loss: 0.2233\n",
      "Train Step: 1644, Loss: 0.4143\n",
      "Train Step: 1645, Loss: 0.2156\n",
      "Train Step: 1646, Loss: 0.3348\n",
      "Train Step: 1647, Loss: 0.1401\n",
      "Train Step: 1648, Loss: 0.3496\n",
      "Train Step: 1649, Loss: 0.2648\n",
      "Train Step: 1650, Loss: 0.1750\n",
      "Train Step: 1651, Loss: 0.2364\n",
      "Train Step: 1652, Loss: 0.3304\n",
      "Train Step: 1653, Loss: 0.2844\n",
      "Train Step: 1654, Loss: 0.3895\n",
      "Train Step: 1655, Loss: 0.3103\n",
      "Train Step: 1656, Loss: 0.2693\n",
      "Train Step: 1657, Loss: 0.3223\n",
      "Train Step: 1658, Loss: 0.2583\n",
      "Train Step: 1659, Loss: 0.3888\n",
      "Train Step: 1660, Loss: 0.3323\n",
      "Train Step: 1661, Loss: 0.3270\n",
      "Train Step: 1662, Loss: 0.5116\n",
      "Train Step: 1663, Loss: 0.2130\n",
      "Train Step: 1664, Loss: 0.2384\n",
      "Train Step: 1665, Loss: 0.1920\n",
      "Train Step: 1666, Loss: 0.3234\n",
      "Train Step: 1667, Loss: 0.2105\n",
      "Train Step: 1668, Loss: 0.3750\n",
      "Train Step: 1669, Loss: 0.4275\n",
      "Train Step: 1670, Loss: 0.3340\n",
      "Train Step: 1671, Loss: 0.3046\n",
      "Train Step: 1672, Loss: 0.2796\n",
      "Train Step: 1673, Loss: 0.3220\n",
      "Train Step: 1674, Loss: 0.3128\n",
      "Train Step: 1675, Loss: 0.1967\n",
      "Train Step: 1676, Loss: 0.2749\n",
      "Train Step: 1677, Loss: 0.3267\n",
      "Train Step: 1678, Loss: 0.1977\n",
      "Train Step: 1679, Loss: 0.2941\n",
      "Train Step: 1680, Loss: 0.4110\n",
      "Train Step: 1681, Loss: 0.3095\n",
      "Train Step: 1682, Loss: 0.4213\n",
      "Train Step: 1683, Loss: 0.2979\n",
      "Train Step: 1684, Loss: 0.2658\n",
      "Train Step: 1685, Loss: 0.3428\n",
      "Train Step: 1686, Loss: 0.3620\n",
      "Train Step: 1687, Loss: 0.2510\n",
      "Train Step: 1688, Loss: 0.2166\n",
      "Train Step: 1689, Loss: 0.2656\n",
      "Train Step: 1690, Loss: 0.1615\n",
      "Train Step: 1691, Loss: 0.3661\n",
      "Train Step: 1692, Loss: 0.2695\n",
      "Train Step: 1693, Loss: 0.2611\n",
      "Train Step: 1694, Loss: 0.5820\n",
      "Train Step: 1695, Loss: 0.2709\n",
      "Train Step: 1696, Loss: 0.2631\n",
      "Train Step: 1697, Loss: 0.5014\n",
      "Train Step: 1698, Loss: 0.3304\n",
      "Train Step: 1699, Loss: 0.3803\n",
      "Train Step: 1700, Loss: 0.1619\n",
      "Train Step: 1701, Loss: 0.2160\n",
      "Train Step: 1702, Loss: 0.2334\n",
      "Train Step: 1703, Loss: 0.3480\n",
      "Train Step: 1704, Loss: 0.3455\n",
      "Train Step: 1705, Loss: 0.2139\n",
      "Train Step: 1706, Loss: 0.3128\n",
      "Train Step: 1707, Loss: 0.2249\n",
      "Train Step: 1708, Loss: 0.3745\n",
      "Train Step: 1709, Loss: 0.3475\n",
      "Train Step: 1710, Loss: 0.2324\n",
      "Train Step: 1711, Loss: 0.2856\n",
      "Train Step: 1712, Loss: 0.3306\n",
      "Train Step: 1713, Loss: 0.2912\n",
      "Train Step: 1714, Loss: 0.2401\n",
      "Train Step: 1715, Loss: 0.2441\n",
      "Train Step: 1716, Loss: 0.1873\n",
      "Train Step: 1717, Loss: 0.2707\n",
      "Train Step: 1718, Loss: 0.2431\n",
      "Train Step: 1719, Loss: 0.1853\n",
      "Train Step: 1720, Loss: 0.3830\n",
      "Train Step: 1721, Loss: 0.3939\n",
      "Train Step: 1722, Loss: 0.1694\n",
      "Train Step: 1723, Loss: 0.2916\n",
      "Train Step: 1724, Loss: 0.3479\n",
      "Train Step: 1725, Loss: 0.2840\n",
      "Train Step: 1726, Loss: 0.3066\n",
      "Train Step: 1727, Loss: 0.2849\n",
      "Train Step: 1728, Loss: 0.3361\n",
      "Train Step: 1729, Loss: 0.3217\n",
      "Train Step: 1730, Loss: 0.3075\n",
      "Train Step: 1731, Loss: 0.2253\n",
      "Train Step: 1732, Loss: 0.2597\n",
      "Train Step: 1733, Loss: 0.2788\n",
      "Train Step: 1734, Loss: 0.2889\n",
      "Train Step: 1735, Loss: 0.2113\n",
      "Train Step: 1736, Loss: 0.2411\n",
      "Train Step: 1737, Loss: 0.2769\n",
      "Train Step: 1738, Loss: 0.2789\n",
      "Train Step: 1739, Loss: 0.3009\n",
      "Train Step: 1740, Loss: 0.3494\n",
      "Train Step: 1741, Loss: 0.3189\n",
      "Train Step: 1742, Loss: 0.3612\n",
      "Train Step: 1743, Loss: 0.2749\n",
      "Train Step: 1744, Loss: 0.3965\n",
      "Train Step: 1745, Loss: 0.1822\n",
      "Train Step: 1746, Loss: 0.3375\n",
      "Train Step: 1747, Loss: 0.2674\n",
      "Train Step: 1748, Loss: 0.2559\n",
      "Train Step: 1749, Loss: 0.3188\n",
      "Train Step: 1750, Loss: 0.2632\n",
      "Train Step: 1751, Loss: 0.4435\n",
      "Train Step: 1752, Loss: 0.2353\n",
      "Train Step: 1753, Loss: 0.2070\n",
      "Train Step: 1754, Loss: 0.2706\n",
      "Train Step: 1755, Loss: 0.2322\n",
      "Train Step: 1756, Loss: 0.2526\n",
      "Train Step: 1757, Loss: 0.2422\n",
      "Train Step: 1758, Loss: 0.1979\n",
      "Train Step: 1759, Loss: 0.3464\n",
      "Train Step: 1760, Loss: 0.2775\n",
      "Train Step: 1761, Loss: 0.2956\n",
      "Train Step: 1762, Loss: 0.1840\n",
      "Train Step: 1763, Loss: 0.3268\n",
      "Train Step: 1764, Loss: 0.3601\n",
      "Train Step: 1765, Loss: 0.2057\n",
      "Train Step: 1766, Loss: 0.2654\n",
      "Train Step: 1767, Loss: 0.2283\n",
      "Train Step: 1768, Loss: 0.2810\n",
      "Train Step: 1769, Loss: 0.2334\n",
      "Train Step: 1770, Loss: 0.2810\n",
      "Train Step: 1771, Loss: 0.2297\n",
      "Train Step: 1772, Loss: 0.1715\n",
      "Train Step: 1773, Loss: 0.3115\n",
      "Train Step: 1774, Loss: 0.3667\n",
      "Train Step: 1775, Loss: 0.2944\n",
      "Train Step: 1776, Loss: 0.3270\n",
      "Train Step: 1777, Loss: 0.1770\n",
      "Train Step: 1778, Loss: 0.1559\n",
      "Train Step: 1779, Loss: 0.1411\n",
      "Train Step: 1780, Loss: 0.1807\n",
      "Train Step: 1781, Loss: 0.3304\n",
      "Train Step: 1782, Loss: 0.1690\n",
      "Train Step: 1783, Loss: 0.3877\n",
      "Train Step: 1784, Loss: 0.2977\n",
      "Train Step: 1785, Loss: 0.1999\n",
      "Train Step: 1786, Loss: 0.2067\n",
      "Train Step: 1787, Loss: 0.2871\n",
      "Train Step: 1788, Loss: 0.2257\n",
      "Train Step: 1789, Loss: 0.3145\n",
      "Train Step: 1790, Loss: 0.2754\n",
      "Train Step: 1791, Loss: 0.2608\n",
      "Train Step: 1792, Loss: 0.3773\n",
      "Train Step: 1793, Loss: 0.2428\n",
      "Train Step: 1794, Loss: 0.3326\n",
      "Train Step: 1795, Loss: 0.2074\n",
      "Train Step: 1796, Loss: 0.2375\n",
      "Train Step: 1797, Loss: 0.2057\n",
      "Train Step: 1798, Loss: 0.3578\n",
      "Train Step: 1799, Loss: 0.3074\n",
      "Train Step: 1800, Loss: 0.2291\n",
      "Train Step: 1801, Loss: 0.3286\n",
      "Train Step: 1802, Loss: 0.3092\n",
      "Train Step: 1803, Loss: 0.3652\n",
      "Train Step: 1804, Loss: 0.3751\n",
      "Train Step: 1805, Loss: 0.4329\n",
      "Train Step: 1806, Loss: 0.2998\n",
      "Train Step: 1807, Loss: 0.2488\n",
      "Train Step: 1808, Loss: 0.1746\n",
      "Train Step: 1809, Loss: 0.3056\n",
      "Train Step: 1810, Loss: 0.2474\n",
      "Train Step: 1811, Loss: 0.2500\n",
      "Train Step: 1812, Loss: 0.3870\n",
      "Train Step: 1813, Loss: 0.2765\n",
      "Train Step: 1814, Loss: 0.2783\n",
      "Train Step: 1815, Loss: 0.3445\n",
      "Train Step: 1816, Loss: 0.3424\n",
      "Train Step: 1817, Loss: 0.5302\n",
      "Train Step: 1818, Loss: 0.2739\n",
      "Train Step: 1819, Loss: 0.2884\n",
      "Train Step: 1820, Loss: 0.3588\n",
      "Train Step: 1821, Loss: 0.3215\n",
      "Train Step: 1822, Loss: 0.3268\n",
      "Train Step: 1823, Loss: 0.2888\n",
      "Train Step: 1824, Loss: 0.1921\n",
      "Train Step: 1825, Loss: 0.3198\n",
      "Train Step: 1826, Loss: 0.3852\n",
      "Train Step: 1827, Loss: 0.4354\n",
      "Train Step: 1828, Loss: 0.1206\n",
      "Train Step: 1829, Loss: 0.3618\n",
      "Train Step: 1830, Loss: 0.2990\n",
      "Train Step: 1831, Loss: 0.3491\n",
      "Train Step: 1832, Loss: 0.2365\n",
      "Train Step: 1833, Loss: 0.2280\n",
      "Train Step: 1834, Loss: 0.3661\n",
      "Train Step: 1835, Loss: 0.2846\n",
      "Train Step: 1836, Loss: 0.2577\n",
      "Train Step: 1837, Loss: 0.4013\n",
      "Train Step: 1838, Loss: 0.2755\n",
      "Train Step: 1839, Loss: 0.2821\n",
      "Train Step: 1840, Loss: 0.3243\n",
      "Train Step: 1841, Loss: 0.2965\n",
      "Train Step: 1842, Loss: 0.2266\n",
      "Train Step: 1843, Loss: 0.2698\n",
      "Train Step: 1844, Loss: 0.2606\n",
      "Train Step: 1845, Loss: 0.0970\n",
      "Train Step: 1846, Loss: 0.3523\n",
      "Train Step: 1847, Loss: 0.4152\n",
      "Train Step: 1848, Loss: 0.3734\n",
      "Train Step: 1849, Loss: 0.2375\n",
      "Train Step: 1850, Loss: 0.2217\n",
      "Train Step: 1851, Loss: 0.2538\n",
      "Train Step: 1852, Loss: 0.3056\n",
      "Train Step: 1853, Loss: 0.3565\n",
      "Train Step: 1854, Loss: 0.3115\n",
      "Train Step: 1855, Loss: 0.2798\n",
      "Train Step: 1856, Loss: 0.3163\n",
      "Train Step: 1857, Loss: 0.3580\n",
      "Train Step: 1858, Loss: 0.1960\n",
      "Train Step: 1859, Loss: 0.3006\n",
      "Train Step: 1860, Loss: 0.2866\n",
      "Train Step: 1861, Loss: 0.2780\n",
      "Train Step: 1862, Loss: 0.2263\n",
      "Train Step: 1863, Loss: 0.2263\n",
      "Train Step: 1864, Loss: 0.1403\n",
      "Train Step: 1865, Loss: 0.3607\n",
      "Train Step: 1866, Loss: 0.3424\n",
      "Train Step: 1867, Loss: 0.2403\n",
      "Train Step: 1868, Loss: 0.2979\n",
      "Train Step: 1869, Loss: 0.2496\n",
      "Train Step: 1870, Loss: 0.2912\n",
      "Train Step: 1871, Loss: 0.1980\n",
      "Train Step: 1872, Loss: 0.3422\n",
      "Train Step: 1873, Loss: 0.2900\n",
      "Train Step: 1874, Loss: 0.3343\n",
      "Train Step: 1875, Loss: 0.1875\n",
      "Train Step: 1876, Loss: 0.4219\n",
      "Train Step: 1877, Loss: 0.3243\n",
      "Train Step: 1878, Loss: 0.2564\n",
      "Train Step: 1879, Loss: 0.2160\n",
      "Train Step: 1880, Loss: 0.3231\n",
      "Train Step: 1881, Loss: 0.2899\n",
      "Train Step: 1882, Loss: 0.2628\n",
      "Train Step: 1883, Loss: 0.3757\n",
      "Train Step: 1884, Loss: 0.2890\n",
      "Train Step: 1885, Loss: 0.2249\n",
      "Train Step: 1886, Loss: 0.2498\n",
      "Train Step: 1887, Loss: 0.1926\n",
      "Train Step: 1888, Loss: 0.3103\n",
      "Train Step: 1889, Loss: 0.1724\n",
      "Train Step: 1890, Loss: 0.2029\n",
      "Train Step: 1891, Loss: 0.2113\n",
      "Train Step: 1892, Loss: 0.2651\n",
      "Train Step: 1893, Loss: 0.2282\n",
      "Train Step: 1894, Loss: 0.3281\n",
      "Train Step: 1895, Loss: 0.1889\n",
      "Train Step: 1896, Loss: 0.1560\n",
      "Train Step: 1897, Loss: 0.3112\n",
      "Train Step: 1898, Loss: 0.3656\n",
      "Train Step: 1899, Loss: 0.2300\n",
      "Train Step: 1900, Loss: 0.2794\n",
      "Train Step: 1901, Loss: 0.2438\n",
      "Train Step: 1902, Loss: 0.2684\n",
      "Train Step: 1903, Loss: 0.4957\n",
      "Train Step: 1904, Loss: 0.3865\n",
      "Train Step: 1905, Loss: 0.4535\n",
      "Train Step: 1906, Loss: 0.2022\n",
      "Train Step: 1907, Loss: 0.2944\n",
      "Train Step: 1908, Loss: 0.4145\n",
      "Train Step: 1909, Loss: 0.2894\n",
      "Train Step: 1910, Loss: 0.2641\n",
      "Train Step: 1911, Loss: 0.4565\n",
      "Train Step: 1912, Loss: 0.3797\n",
      "Train Step: 1913, Loss: 0.3732\n",
      "Train Step: 1914, Loss: 0.2305\n",
      "Train Step: 1915, Loss: 0.2918\n",
      "Train Step: 1916, Loss: 0.2848\n",
      "Train Step: 1917, Loss: 0.4087\n",
      "Train Step: 1918, Loss: 0.1458\n",
      "Train Step: 1919, Loss: 0.2364\n",
      "Train Step: 1920, Loss: 0.4694\n",
      "Train Step: 1921, Loss: 0.2836\n",
      "Train Step: 1922, Loss: 0.2104\n",
      "Train Step: 1923, Loss: 0.3094\n",
      "Train Step: 1924, Loss: 0.3569\n",
      "Train Step: 1925, Loss: 0.3366\n",
      "Train Step: 1926, Loss: 0.2010\n",
      "Train Step: 1927, Loss: 0.2347\n",
      "Train Step: 1928, Loss: 0.2474\n",
      "Train Step: 1929, Loss: 0.3481\n",
      "Train Step: 1930, Loss: 0.2127\n",
      "Train Step: 1931, Loss: 0.4389\n",
      "Train Step: 1932, Loss: 0.3278\n",
      "Train Step: 1933, Loss: 0.3027\n",
      "Train Step: 1934, Loss: 0.2598\n",
      "Train Step: 1935, Loss: 0.4480\n",
      "Train Step: 1936, Loss: 0.3581\n",
      "Train Step: 1937, Loss: 0.2580\n",
      "Train Step: 1938, Loss: 0.1826\n",
      "Train Step: 1939, Loss: 0.2755\n",
      "Train Step: 1940, Loss: 0.2430\n",
      "Train Step: 1941, Loss: 0.2510\n",
      "Train Step: 1942, Loss: 0.3050\n",
      "Train Step: 1943, Loss: 0.1877\n",
      "Train Step: 1944, Loss: 0.3527\n",
      "Train Step: 1945, Loss: 0.3203\n",
      "Train Step: 1946, Loss: 0.2614\n",
      "Train Step: 1947, Loss: 0.2129\n",
      "Train Step: 1948, Loss: 0.4417\n",
      "Train Step: 1949, Loss: 0.2769\n",
      "Train Step: 1950, Loss: 0.1161\n",
      "Train Step: 1951, Loss: 0.3874\n",
      "Train Step: 1952, Loss: 0.2744\n",
      "Train Step: 1953, Loss: 0.2909\n",
      "Train Step: 1954, Loss: 0.4106\n",
      "Train Step: 1955, Loss: 0.2496\n",
      "Epoch [5/10], Loss: 0.2902\n",
      "Train Step: 1956, Loss: 0.1620\n",
      "Train Step: 1957, Loss: 0.2142\n",
      "Train Step: 1958, Loss: 0.2656\n",
      "Train Step: 1959, Loss: 0.3192\n",
      "Train Step: 1960, Loss: 0.2284\n",
      "Train Step: 1961, Loss: 0.2073\n",
      "Train Step: 1962, Loss: 0.2440\n",
      "Train Step: 1963, Loss: 0.2748\n",
      "Train Step: 1964, Loss: 0.1804\n",
      "Train Step: 1965, Loss: 0.3095\n",
      "Train Step: 1966, Loss: 0.2054\n",
      "Train Step: 1967, Loss: 0.2696\n",
      "Train Step: 1968, Loss: 0.2996\n",
      "Train Step: 1969, Loss: 0.2994\n",
      "Train Step: 1970, Loss: 0.1380\n",
      "Train Step: 1971, Loss: 0.1794\n",
      "Train Step: 1972, Loss: 0.2449\n",
      "Train Step: 1973, Loss: 0.1897\n",
      "Train Step: 1974, Loss: 0.2687\n",
      "Train Step: 1975, Loss: 0.2314\n",
      "Train Step: 1976, Loss: 0.3365\n",
      "Train Step: 1977, Loss: 0.1782\n",
      "Train Step: 1978, Loss: 0.2086\n",
      "Train Step: 1979, Loss: 0.1708\n",
      "Train Step: 1980, Loss: 0.2569\n",
      "Train Step: 1981, Loss: 0.2631\n",
      "Train Step: 1982, Loss: 0.2022\n",
      "Train Step: 1983, Loss: 0.2514\n",
      "Train Step: 1984, Loss: 0.2734\n",
      "Train Step: 1985, Loss: 0.3447\n",
      "Train Step: 1986, Loss: 0.3380\n",
      "Train Step: 1987, Loss: 0.1611\n",
      "Train Step: 1988, Loss: 0.3617\n",
      "Train Step: 1989, Loss: 0.1862\n",
      "Train Step: 1990, Loss: 0.2832\n",
      "Train Step: 1991, Loss: 0.3026\n",
      "Train Step: 1992, Loss: 0.1545\n",
      "Train Step: 1993, Loss: 0.3685\n",
      "Train Step: 1994, Loss: 0.2285\n",
      "Train Step: 1995, Loss: 0.3028\n",
      "Train Step: 1996, Loss: 0.2279\n",
      "Train Step: 1997, Loss: 0.3028\n",
      "Train Step: 1998, Loss: 0.1937\n",
      "Train Step: 1999, Loss: 0.2209\n",
      "Train Step: 2000, Loss: 0.1120\n",
      "Train Step: 2001, Loss: 0.2583\n",
      "Train Step: 2002, Loss: 0.2459\n",
      "Train Step: 2003, Loss: 0.2950\n",
      "Train Step: 2004, Loss: 0.2462\n",
      "Train Step: 2005, Loss: 0.2430\n",
      "Train Step: 2006, Loss: 0.3478\n",
      "Train Step: 2007, Loss: 0.2960\n",
      "Train Step: 2008, Loss: 0.1775\n",
      "Train Step: 2009, Loss: 0.1911\n",
      "Train Step: 2010, Loss: 0.2713\n",
      "Train Step: 2011, Loss: 0.2229\n",
      "Train Step: 2012, Loss: 0.2361\n",
      "Train Step: 2013, Loss: 0.2810\n",
      "Train Step: 2014, Loss: 0.1776\n",
      "Train Step: 2015, Loss: 0.1638\n",
      "Train Step: 2016, Loss: 0.2750\n",
      "Train Step: 2017, Loss: 0.2631\n",
      "Train Step: 2018, Loss: 0.2716\n",
      "Train Step: 2019, Loss: 0.1666\n",
      "Train Step: 2020, Loss: 0.2787\n",
      "Train Step: 2021, Loss: 0.2569\n",
      "Train Step: 2022, Loss: 0.2724\n",
      "Train Step: 2023, Loss: 0.1997\n",
      "Train Step: 2024, Loss: 0.2535\n",
      "Train Step: 2025, Loss: 0.2768\n",
      "Train Step: 2026, Loss: 0.1672\n",
      "Train Step: 2027, Loss: 0.1794\n",
      "Train Step: 2028, Loss: 0.1914\n",
      "Train Step: 2029, Loss: 0.2112\n",
      "Train Step: 2030, Loss: 0.1268\n",
      "Train Step: 2031, Loss: 0.2270\n",
      "Train Step: 2032, Loss: 0.2059\n",
      "Train Step: 2033, Loss: 0.1624\n",
      "Train Step: 2034, Loss: 0.2212\n",
      "Train Step: 2035, Loss: 0.2411\n",
      "Train Step: 2036, Loss: 0.2318\n",
      "Train Step: 2037, Loss: 0.2920\n",
      "Train Step: 2038, Loss: 0.2241\n",
      "Train Step: 2039, Loss: 0.2004\n",
      "Train Step: 2040, Loss: 0.2204\n",
      "Train Step: 2041, Loss: 0.1294\n",
      "Train Step: 2042, Loss: 0.2291\n",
      "Train Step: 2043, Loss: 0.2364\n",
      "Train Step: 2044, Loss: 0.3270\n",
      "Train Step: 2045, Loss: 0.1795\n",
      "Train Step: 2046, Loss: 0.1614\n",
      "Train Step: 2047, Loss: 0.1852\n",
      "Train Step: 2048, Loss: 0.2034\n",
      "Train Step: 2049, Loss: 0.2201\n",
      "Train Step: 2050, Loss: 0.2714\n",
      "Train Step: 2051, Loss: 0.1438\n",
      "Train Step: 2052, Loss: 0.2630\n",
      "Train Step: 2053, Loss: 0.2872\n",
      "Train Step: 2054, Loss: 0.2492\n",
      "Train Step: 2055, Loss: 0.1845\n",
      "Train Step: 2056, Loss: 0.1792\n",
      "Train Step: 2057, Loss: 0.2153\n",
      "Train Step: 2058, Loss: 0.1474\n",
      "Train Step: 2059, Loss: 0.1442\n",
      "Train Step: 2060, Loss: 0.3297\n",
      "Train Step: 2061, Loss: 0.2172\n",
      "Train Step: 2062, Loss: 0.2690\n",
      "Train Step: 2063, Loss: 0.1405\n",
      "Train Step: 2064, Loss: 0.4145\n",
      "Train Step: 2065, Loss: 0.4010\n",
      "Train Step: 2066, Loss: 0.1821\n",
      "Train Step: 2067, Loss: 0.1485\n",
      "Train Step: 2068, Loss: 0.2253\n",
      "Train Step: 2069, Loss: 0.4646\n",
      "Train Step: 2070, Loss: 0.1592\n",
      "Train Step: 2071, Loss: 0.2416\n",
      "Train Step: 2072, Loss: 0.2735\n",
      "Train Step: 2073, Loss: 0.1967\n",
      "Train Step: 2074, Loss: 0.2344\n",
      "Train Step: 2075, Loss: 0.1772\n",
      "Train Step: 2076, Loss: 0.1912\n",
      "Train Step: 2077, Loss: 0.2280\n",
      "Train Step: 2078, Loss: 0.4437\n",
      "Train Step: 2079, Loss: 0.2729\n",
      "Train Step: 2080, Loss: 0.1907\n",
      "Train Step: 2081, Loss: 0.1653\n",
      "Train Step: 2082, Loss: 0.2309\n",
      "Train Step: 2083, Loss: 0.2117\n",
      "Train Step: 2084, Loss: 0.1948\n",
      "Train Step: 2085, Loss: 0.3239\n",
      "Train Step: 2086, Loss: 0.2882\n",
      "Train Step: 2087, Loss: 0.1577\n",
      "Train Step: 2088, Loss: 0.2721\n",
      "Train Step: 2089, Loss: 0.2091\n",
      "Train Step: 2090, Loss: 0.3330\n",
      "Train Step: 2091, Loss: 0.1819\n",
      "Train Step: 2092, Loss: 0.1661\n",
      "Train Step: 2093, Loss: 0.1630\n",
      "Train Step: 2094, Loss: 0.2096\n",
      "Train Step: 2095, Loss: 0.2996\n",
      "Train Step: 2096, Loss: 0.2812\n",
      "Train Step: 2097, Loss: 0.2199\n",
      "Train Step: 2098, Loss: 0.3149\n",
      "Train Step: 2099, Loss: 0.2504\n",
      "Train Step: 2100, Loss: 0.2983\n",
      "Train Step: 2101, Loss: 0.2575\n",
      "Train Step: 2102, Loss: 0.2077\n",
      "Train Step: 2103, Loss: 0.2103\n",
      "Train Step: 2104, Loss: 0.2427\n",
      "Train Step: 2105, Loss: 0.3491\n",
      "Train Step: 2106, Loss: 0.2218\n",
      "Train Step: 2107, Loss: 0.2052\n",
      "Train Step: 2108, Loss: 0.1705\n",
      "Train Step: 2109, Loss: 0.1353\n",
      "Train Step: 2110, Loss: 0.1773\n",
      "Train Step: 2111, Loss: 0.1944\n",
      "Train Step: 2112, Loss: 0.2259\n",
      "Train Step: 2113, Loss: 0.2220\n",
      "Train Step: 2114, Loss: 0.1318\n",
      "Train Step: 2115, Loss: 0.1332\n",
      "Train Step: 2116, Loss: 0.1664\n",
      "Train Step: 2117, Loss: 0.1762\n",
      "Train Step: 2118, Loss: 0.1499\n",
      "Train Step: 2119, Loss: 0.2311\n",
      "Train Step: 2120, Loss: 0.3451\n",
      "Train Step: 2121, Loss: 0.3859\n",
      "Train Step: 2122, Loss: 0.1913\n",
      "Train Step: 2123, Loss: 0.2613\n",
      "Train Step: 2124, Loss: 0.1382\n",
      "Train Step: 2125, Loss: 0.2992\n",
      "Train Step: 2126, Loss: 0.2116\n",
      "Train Step: 2127, Loss: 0.2555\n",
      "Train Step: 2128, Loss: 0.3774\n",
      "Train Step: 2129, Loss: 0.1050\n",
      "Train Step: 2130, Loss: 0.1487\n",
      "Train Step: 2131, Loss: 0.1847\n",
      "Train Step: 2132, Loss: 0.1828\n",
      "Train Step: 2133, Loss: 0.3054\n",
      "Train Step: 2134, Loss: 0.2811\n",
      "Train Step: 2135, Loss: 0.2171\n",
      "Train Step: 2136, Loss: 0.2666\n",
      "Train Step: 2137, Loss: 0.2734\n",
      "Train Step: 2138, Loss: 0.3734\n",
      "Train Step: 2139, Loss: 0.1866\n",
      "Train Step: 2140, Loss: 0.3790\n",
      "Train Step: 2141, Loss: 0.2462\n",
      "Train Step: 2142, Loss: 0.2141\n",
      "Train Step: 2143, Loss: 0.2767\n",
      "Train Step: 2144, Loss: 0.2618\n",
      "Train Step: 2145, Loss: 0.1548\n",
      "Train Step: 2146, Loss: 0.2040\n",
      "Train Step: 2147, Loss: 0.2111\n",
      "Train Step: 2148, Loss: 0.1727\n",
      "Train Step: 2149, Loss: 0.2215\n",
      "Train Step: 2150, Loss: 0.3084\n",
      "Train Step: 2151, Loss: 0.1868\n",
      "Train Step: 2152, Loss: 0.2347\n",
      "Train Step: 2153, Loss: 0.1671\n",
      "Train Step: 2154, Loss: 0.5119\n",
      "Train Step: 2155, Loss: 0.2892\n",
      "Train Step: 2156, Loss: 0.1347\n",
      "Train Step: 2157, Loss: 0.2251\n",
      "Train Step: 2158, Loss: 0.2671\n",
      "Train Step: 2159, Loss: 0.3122\n",
      "Train Step: 2160, Loss: 0.2057\n",
      "Train Step: 2161, Loss: 0.1084\n",
      "Train Step: 2162, Loss: 0.2916\n",
      "Train Step: 2163, Loss: 0.2464\n",
      "Train Step: 2164, Loss: 0.2162\n",
      "Train Step: 2165, Loss: 0.2016\n",
      "Train Step: 2166, Loss: 0.1996\n",
      "Train Step: 2167, Loss: 0.2121\n",
      "Train Step: 2168, Loss: 0.2276\n",
      "Train Step: 2169, Loss: 0.1278\n",
      "Train Step: 2170, Loss: 0.2230\n",
      "Train Step: 2171, Loss: 0.2695\n",
      "Train Step: 2172, Loss: 0.4640\n",
      "Train Step: 2173, Loss: 0.2275\n",
      "Train Step: 2174, Loss: 0.2890\n",
      "Train Step: 2175, Loss: 0.3142\n",
      "Train Step: 2176, Loss: 0.1849\n",
      "Train Step: 2177, Loss: 0.2422\n",
      "Train Step: 2178, Loss: 0.2299\n",
      "Train Step: 2179, Loss: 0.2879\n",
      "Train Step: 2180, Loss: 0.2591\n",
      "Train Step: 2181, Loss: 0.2513\n",
      "Train Step: 2182, Loss: 0.2532\n",
      "Train Step: 2183, Loss: 0.2421\n",
      "Train Step: 2184, Loss: 0.2865\n",
      "Train Step: 2185, Loss: 0.2222\n",
      "Train Step: 2186, Loss: 0.1620\n",
      "Train Step: 2187, Loss: 0.3794\n",
      "Train Step: 2188, Loss: 0.2432\n",
      "Train Step: 2189, Loss: 0.1744\n",
      "Train Step: 2190, Loss: 0.2365\n",
      "Train Step: 2191, Loss: 0.2421\n",
      "Train Step: 2192, Loss: 0.2603\n",
      "Train Step: 2193, Loss: 0.2001\n",
      "Train Step: 2194, Loss: 0.2181\n",
      "Train Step: 2195, Loss: 0.2279\n",
      "Train Step: 2196, Loss: 0.2175\n",
      "Train Step: 2197, Loss: 0.1947\n",
      "Train Step: 2198, Loss: 0.1853\n",
      "Train Step: 2199, Loss: 0.1832\n",
      "Train Step: 2200, Loss: 0.2590\n",
      "Train Step: 2201, Loss: 0.3536\n",
      "Train Step: 2202, Loss: 0.2985\n",
      "Train Step: 2203, Loss: 0.2886\n",
      "Train Step: 2204, Loss: 0.3262\n",
      "Train Step: 2205, Loss: 0.1908\n",
      "Train Step: 2206, Loss: 0.1544\n",
      "Train Step: 2207, Loss: 0.2783\n",
      "Train Step: 2208, Loss: 0.1988\n",
      "Train Step: 2209, Loss: 0.3513\n",
      "Train Step: 2210, Loss: 0.2133\n",
      "Train Step: 2211, Loss: 0.1740\n",
      "Train Step: 2212, Loss: 0.2161\n",
      "Train Step: 2213, Loss: 0.1659\n",
      "Train Step: 2214, Loss: 0.1819\n",
      "Train Step: 2215, Loss: 0.2223\n",
      "Train Step: 2216, Loss: 0.1853\n",
      "Train Step: 2217, Loss: 0.1577\n",
      "Train Step: 2218, Loss: 0.4341\n",
      "Train Step: 2219, Loss: 0.1771\n",
      "Train Step: 2220, Loss: 0.1400\n",
      "Train Step: 2221, Loss: 0.3274\n",
      "Train Step: 2222, Loss: 0.2261\n",
      "Train Step: 2223, Loss: 0.2979\n",
      "Train Step: 2224, Loss: 0.3307\n",
      "Train Step: 2225, Loss: 0.3170\n",
      "Train Step: 2226, Loss: 0.2200\n",
      "Train Step: 2227, Loss: 0.1889\n",
      "Train Step: 2228, Loss: 0.2874\n",
      "Train Step: 2229, Loss: 0.2561\n",
      "Train Step: 2230, Loss: 0.2023\n",
      "Train Step: 2231, Loss: 0.2460\n",
      "Train Step: 2232, Loss: 0.3007\n",
      "Train Step: 2233, Loss: 0.2677\n",
      "Train Step: 2234, Loss: 0.4845\n",
      "Train Step: 2235, Loss: 0.2179\n",
      "Train Step: 2236, Loss: 0.1683\n",
      "Train Step: 2237, Loss: 0.2591\n",
      "Train Step: 2238, Loss: 0.3513\n",
      "Train Step: 2239, Loss: 0.1066\n",
      "Train Step: 2240, Loss: 0.2295\n",
      "Train Step: 2241, Loss: 0.2506\n",
      "Train Step: 2242, Loss: 0.2329\n",
      "Train Step: 2243, Loss: 0.1795\n",
      "Train Step: 2244, Loss: 0.2412\n",
      "Train Step: 2245, Loss: 0.2216\n",
      "Train Step: 2246, Loss: 0.2567\n",
      "Train Step: 2247, Loss: 0.2628\n",
      "Train Step: 2248, Loss: 0.2675\n",
      "Train Step: 2249, Loss: 0.2929\n",
      "Train Step: 2250, Loss: 0.2848\n",
      "Train Step: 2251, Loss: 0.1711\n",
      "Train Step: 2252, Loss: 0.2141\n",
      "Train Step: 2253, Loss: 0.2999\n",
      "Train Step: 2254, Loss: 0.2163\n",
      "Train Step: 2255, Loss: 0.2293\n",
      "Train Step: 2256, Loss: 0.4397\n",
      "Train Step: 2257, Loss: 0.2079\n",
      "Train Step: 2258, Loss: 0.1951\n",
      "Train Step: 2259, Loss: 0.2134\n",
      "Train Step: 2260, Loss: 0.2307\n",
      "Train Step: 2261, Loss: 0.1415\n",
      "Train Step: 2262, Loss: 0.3311\n",
      "Train Step: 2263, Loss: 0.1256\n",
      "Train Step: 2264, Loss: 0.2870\n",
      "Train Step: 2265, Loss: 0.1809\n",
      "Train Step: 2266, Loss: 0.2104\n",
      "Train Step: 2267, Loss: 0.2208\n",
      "Train Step: 2268, Loss: 0.3210\n",
      "Train Step: 2269, Loss: 0.3669\n",
      "Train Step: 2270, Loss: 0.2360\n",
      "Train Step: 2271, Loss: 0.1934\n",
      "Train Step: 2272, Loss: 0.1665\n",
      "Train Step: 2273, Loss: 0.2743\n",
      "Train Step: 2274, Loss: 0.2338\n",
      "Train Step: 2275, Loss: 0.3019\n",
      "Train Step: 2276, Loss: 0.1515\n",
      "Train Step: 2277, Loss: 0.1885\n",
      "Train Step: 2278, Loss: 0.2706\n",
      "Train Step: 2279, Loss: 0.1178\n",
      "Train Step: 2280, Loss: 0.1672\n",
      "Train Step: 2281, Loss: 0.2952\n",
      "Train Step: 2282, Loss: 0.2592\n",
      "Train Step: 2283, Loss: 0.1986\n",
      "Train Step: 2284, Loss: 0.1847\n",
      "Train Step: 2285, Loss: 0.2062\n",
      "Train Step: 2286, Loss: 0.2165\n",
      "Train Step: 2287, Loss: 0.2510\n",
      "Train Step: 2288, Loss: 0.1696\n",
      "Train Step: 2289, Loss: 0.2717\n",
      "Train Step: 2290, Loss: 0.3754\n",
      "Train Step: 2291, Loss: 0.2101\n",
      "Train Step: 2292, Loss: 0.1583\n",
      "Train Step: 2293, Loss: 0.3143\n",
      "Train Step: 2294, Loss: 0.2456\n",
      "Train Step: 2295, Loss: 0.2097\n",
      "Train Step: 2296, Loss: 0.3967\n",
      "Train Step: 2297, Loss: 0.2521\n",
      "Train Step: 2298, Loss: 0.1562\n",
      "Train Step: 2299, Loss: 0.2239\n",
      "Train Step: 2300, Loss: 0.2901\n",
      "Train Step: 2301, Loss: 0.1644\n",
      "Train Step: 2302, Loss: 0.2065\n",
      "Train Step: 2303, Loss: 0.2005\n",
      "Train Step: 2304, Loss: 0.2236\n",
      "Train Step: 2305, Loss: 0.2273\n",
      "Train Step: 2306, Loss: 0.1932\n",
      "Train Step: 2307, Loss: 0.1918\n",
      "Train Step: 2308, Loss: 0.2224\n",
      "Train Step: 2309, Loss: 0.3132\n",
      "Train Step: 2310, Loss: 0.2461\n",
      "Train Step: 2311, Loss: 0.3449\n",
      "Train Step: 2312, Loss: 0.2287\n",
      "Train Step: 2313, Loss: 0.2095\n",
      "Train Step: 2314, Loss: 0.2155\n",
      "Train Step: 2315, Loss: 0.3519\n",
      "Train Step: 2316, Loss: 0.1316\n",
      "Train Step: 2317, Loss: 0.2730\n",
      "Train Step: 2318, Loss: 0.3003\n",
      "Train Step: 2319, Loss: 0.1700\n",
      "Train Step: 2320, Loss: 0.2847\n",
      "Train Step: 2321, Loss: 0.1901\n",
      "Train Step: 2322, Loss: 0.4538\n",
      "Train Step: 2323, Loss: 0.2692\n",
      "Train Step: 2324, Loss: 0.3907\n",
      "Train Step: 2325, Loss: 0.3791\n",
      "Train Step: 2326, Loss: 0.4206\n",
      "Train Step: 2327, Loss: 0.2270\n",
      "Train Step: 2328, Loss: 0.2047\n",
      "Train Step: 2329, Loss: 0.2126\n",
      "Train Step: 2330, Loss: 0.2041\n",
      "Train Step: 2331, Loss: 0.1700\n",
      "Train Step: 2332, Loss: 0.3655\n",
      "Train Step: 2333, Loss: 0.2151\n",
      "Train Step: 2334, Loss: 0.1846\n",
      "Train Step: 2335, Loss: 0.2113\n",
      "Train Step: 2336, Loss: 0.3005\n",
      "Train Step: 2337, Loss: 0.1577\n",
      "Train Step: 2338, Loss: 0.2506\n",
      "Train Step: 2339, Loss: 0.2033\n",
      "Train Step: 2340, Loss: 0.2900\n",
      "Train Step: 2341, Loss: 0.2546\n",
      "Train Step: 2342, Loss: 0.1920\n",
      "Train Step: 2343, Loss: 0.2229\n",
      "Train Step: 2344, Loss: 0.2266\n",
      "Train Step: 2345, Loss: 0.1995\n",
      "Train Step: 2346, Loss: 0.5664\n",
      "Epoch [6/10], Loss: 0.2398\n",
      "Train Step: 2347, Loss: 0.1579\n",
      "Train Step: 2348, Loss: 0.1966\n",
      "Train Step: 2349, Loss: 0.2042\n",
      "Train Step: 2350, Loss: 0.1984\n",
      "Train Step: 2351, Loss: 0.1419\n",
      "Train Step: 2352, Loss: 0.0794\n",
      "Train Step: 2353, Loss: 0.0903\n",
      "Train Step: 2354, Loss: 0.2831\n",
      "Train Step: 2355, Loss: 0.2099\n",
      "Train Step: 2356, Loss: 0.2232\n",
      "Train Step: 2357, Loss: 0.1255\n",
      "Train Step: 2358, Loss: 0.1142\n",
      "Train Step: 2359, Loss: 0.1686\n",
      "Train Step: 2360, Loss: 0.0959\n",
      "Train Step: 2361, Loss: 0.2151\n",
      "Train Step: 2362, Loss: 0.0995\n",
      "Train Step: 2363, Loss: 0.1927\n",
      "Train Step: 2364, Loss: 0.1426\n",
      "Train Step: 2365, Loss: 0.1539\n",
      "Train Step: 2366, Loss: 0.1355\n",
      "Train Step: 2367, Loss: 0.1319\n",
      "Train Step: 2368, Loss: 0.1856\n",
      "Train Step: 2369, Loss: 0.1943\n",
      "Train Step: 2370, Loss: 0.1825\n",
      "Train Step: 2371, Loss: 0.1300\n",
      "Train Step: 2372, Loss: 0.1413\n",
      "Train Step: 2373, Loss: 0.1146\n",
      "Train Step: 2374, Loss: 0.0857\n",
      "Train Step: 2375, Loss: 0.1123\n",
      "Train Step: 2376, Loss: 0.1881\n",
      "Train Step: 2377, Loss: 0.1173\n",
      "Train Step: 2378, Loss: 0.1274\n",
      "Train Step: 2379, Loss: 0.2436\n",
      "Train Step: 2380, Loss: 0.1990\n",
      "Train Step: 2381, Loss: 0.2878\n",
      "Train Step: 2382, Loss: 0.1707\n",
      "Train Step: 2383, Loss: 0.2231\n",
      "Train Step: 2384, Loss: 0.0941\n",
      "Train Step: 2385, Loss: 0.2171\n",
      "Train Step: 2386, Loss: 0.1422\n",
      "Train Step: 2387, Loss: 0.1841\n",
      "Train Step: 2388, Loss: 0.2334\n",
      "Train Step: 2389, Loss: 0.1987\n",
      "Train Step: 2390, Loss: 0.1948\n",
      "Train Step: 2391, Loss: 0.1137\n",
      "Train Step: 2392, Loss: 0.1004\n",
      "Train Step: 2393, Loss: 0.1036\n",
      "Train Step: 2394, Loss: 0.1567\n",
      "Train Step: 2395, Loss: 0.0470\n",
      "Train Step: 2396, Loss: 0.2024\n",
      "Train Step: 2397, Loss: 0.0755\n",
      "Train Step: 2398, Loss: 0.1062\n",
      "Train Step: 2399, Loss: 0.1185\n",
      "Train Step: 2400, Loss: 0.2204\n",
      "Train Step: 2401, Loss: 0.1724\n",
      "Train Step: 2402, Loss: 0.1586\n",
      "Train Step: 2403, Loss: 0.1858\n",
      "Train Step: 2404, Loss: 0.1580\n",
      "Train Step: 2405, Loss: 0.1690\n",
      "Train Step: 2406, Loss: 0.1335\n",
      "Train Step: 2407, Loss: 0.1609\n",
      "Train Step: 2408, Loss: 0.2741\n",
      "Train Step: 2409, Loss: 0.1574\n",
      "Train Step: 2410, Loss: 0.1346\n",
      "Train Step: 2411, Loss: 0.2418\n",
      "Train Step: 2412, Loss: 0.2146\n",
      "Train Step: 2413, Loss: 0.1061\n",
      "Train Step: 2414, Loss: 0.2351\n",
      "Train Step: 2415, Loss: 0.1343\n",
      "Train Step: 2416, Loss: 0.1300\n",
      "Train Step: 2417, Loss: 0.1530\n",
      "Train Step: 2418, Loss: 0.1940\n",
      "Train Step: 2419, Loss: 0.2540\n",
      "Train Step: 2420, Loss: 0.2147\n",
      "Train Step: 2421, Loss: 0.1755\n",
      "Train Step: 2422, Loss: 0.2207\n",
      "Train Step: 2423, Loss: 0.1539\n",
      "Train Step: 2424, Loss: 0.1127\n",
      "Train Step: 2425, Loss: 0.0768\n",
      "Train Step: 2426, Loss: 0.2500\n",
      "Train Step: 2427, Loss: 0.1368\n",
      "Train Step: 2428, Loss: 0.1632\n",
      "Train Step: 2429, Loss: 0.1725\n",
      "Train Step: 2430, Loss: 0.1610\n",
      "Train Step: 2431, Loss: 0.0927\n",
      "Train Step: 2432, Loss: 0.1691\n",
      "Train Step: 2433, Loss: 0.2033\n",
      "Train Step: 2434, Loss: 0.2407\n",
      "Train Step: 2435, Loss: 0.0456\n",
      "Train Step: 2436, Loss: 0.2148\n",
      "Train Step: 2437, Loss: 0.2214\n",
      "Train Step: 2438, Loss: 0.1760\n",
      "Train Step: 2439, Loss: 0.1776\n",
      "Train Step: 2440, Loss: 0.1594\n",
      "Train Step: 2441, Loss: 0.1363\n",
      "Train Step: 2442, Loss: 0.2398\n",
      "Train Step: 2443, Loss: 0.1562\n",
      "Train Step: 2444, Loss: 0.2139\n",
      "Train Step: 2445, Loss: 0.2177\n",
      "Train Step: 2446, Loss: 0.2441\n",
      "Train Step: 2447, Loss: 0.1653\n",
      "Train Step: 2448, Loss: 0.3030\n",
      "Train Step: 2449, Loss: 0.1562\n",
      "Train Step: 2450, Loss: 0.2243\n",
      "Train Step: 2451, Loss: 0.1314\n",
      "Train Step: 2452, Loss: 0.1824\n",
      "Train Step: 2453, Loss: 0.1619\n",
      "Train Step: 2454, Loss: 0.2083\n",
      "Train Step: 2455, Loss: 0.1188\n",
      "Train Step: 2456, Loss: 0.0849\n",
      "Train Step: 2457, Loss: 0.1317\n",
      "Train Step: 2458, Loss: 0.2184\n",
      "Train Step: 2459, Loss: 0.1063\n",
      "Train Step: 2460, Loss: 0.2340\n",
      "Train Step: 2461, Loss: 0.1958\n",
      "Train Step: 2462, Loss: 0.1712\n",
      "Train Step: 2463, Loss: 0.1685\n",
      "Train Step: 2464, Loss: 0.2512\n",
      "Train Step: 2465, Loss: 0.2360\n",
      "Train Step: 2466, Loss: 0.2691\n",
      "Train Step: 2467, Loss: 0.1892\n",
      "Train Step: 2468, Loss: 0.0814\n",
      "Train Step: 2469, Loss: 0.1235\n",
      "Train Step: 2470, Loss: 0.1663\n",
      "Train Step: 2471, Loss: 0.1266\n",
      "Train Step: 2472, Loss: 0.1057\n",
      "Train Step: 2473, Loss: 0.2122\n",
      "Train Step: 2474, Loss: 0.1573\n",
      "Train Step: 2475, Loss: 0.2063\n",
      "Train Step: 2476, Loss: 0.2161\n",
      "Train Step: 2477, Loss: 0.2523\n",
      "Train Step: 2478, Loss: 0.1347\n",
      "Train Step: 2479, Loss: 0.1576\n",
      "Train Step: 2480, Loss: 0.0675\n",
      "Train Step: 2481, Loss: 0.1302\n",
      "Train Step: 2482, Loss: 0.1609\n",
      "Train Step: 2483, Loss: 0.2717\n",
      "Train Step: 2484, Loss: 0.1522\n",
      "Train Step: 2485, Loss: 0.2109\n",
      "Train Step: 2486, Loss: 0.2610\n",
      "Train Step: 2487, Loss: 0.1341\n",
      "Train Step: 2488, Loss: 0.1563\n",
      "Train Step: 2489, Loss: 0.0813\n",
      "Train Step: 2490, Loss: 0.1976\n",
      "Train Step: 2491, Loss: 0.1082\n",
      "Train Step: 2492, Loss: 0.2236\n",
      "Train Step: 2493, Loss: 0.1474\n",
      "Train Step: 2494, Loss: 0.1981\n",
      "Train Step: 2495, Loss: 0.1415\n",
      "Train Step: 2496, Loss: 0.2959\n",
      "Train Step: 2497, Loss: 0.1142\n",
      "Train Step: 2498, Loss: 0.2152\n",
      "Train Step: 2499, Loss: 0.2150\n",
      "Train Step: 2500, Loss: 0.3217\n",
      "Train Step: 2501, Loss: 0.2047\n",
      "Train Step: 2502, Loss: 0.2600\n",
      "Train Step: 2503, Loss: 0.1039\n",
      "Train Step: 2504, Loss: 0.2167\n",
      "Train Step: 2505, Loss: 0.2862\n",
      "Train Step: 2506, Loss: 0.2259\n",
      "Train Step: 2507, Loss: 0.2036\n",
      "Train Step: 2508, Loss: 0.0779\n",
      "Train Step: 2509, Loss: 0.1627\n",
      "Train Step: 2510, Loss: 0.1622\n",
      "Train Step: 2511, Loss: 0.0948\n",
      "Train Step: 2512, Loss: 0.2432\n",
      "Train Step: 2513, Loss: 0.1465\n",
      "Train Step: 2514, Loss: 0.1566\n",
      "Train Step: 2515, Loss: 0.1428\n",
      "Train Step: 2516, Loss: 0.2011\n",
      "Train Step: 2517, Loss: 0.1430\n",
      "Train Step: 2518, Loss: 0.1141\n",
      "Train Step: 2519, Loss: 0.1255\n",
      "Train Step: 2520, Loss: 0.1327\n",
      "Train Step: 2521, Loss: 0.2401\n",
      "Train Step: 2522, Loss: 0.1201\n",
      "Train Step: 2523, Loss: 0.0720\n",
      "Train Step: 2524, Loss: 0.1476\n",
      "Train Step: 2525, Loss: 0.1504\n",
      "Train Step: 2526, Loss: 0.2464\n",
      "Train Step: 2527, Loss: 0.2289\n",
      "Train Step: 2528, Loss: 0.1117\n",
      "Train Step: 2529, Loss: 0.1015\n",
      "Train Step: 2530, Loss: 0.1497\n",
      "Train Step: 2531, Loss: 0.1233\n",
      "Train Step: 2532, Loss: 0.1906\n",
      "Train Step: 2533, Loss: 0.2474\n",
      "Train Step: 2534, Loss: 0.1454\n",
      "Train Step: 2535, Loss: 0.1897\n",
      "Train Step: 2536, Loss: 0.1174\n",
      "Train Step: 2537, Loss: 0.2501\n",
      "Train Step: 2538, Loss: 0.1285\n",
      "Train Step: 2539, Loss: 0.2348\n",
      "Train Step: 2540, Loss: 0.1560\n",
      "Train Step: 2541, Loss: 0.2758\n",
      "Train Step: 2542, Loss: 0.2199\n",
      "Train Step: 2543, Loss: 0.2078\n",
      "Train Step: 2544, Loss: 0.2570\n",
      "Train Step: 2545, Loss: 0.2801\n",
      "Train Step: 2546, Loss: 0.2248\n",
      "Train Step: 2547, Loss: 0.2612\n",
      "Train Step: 2548, Loss: 0.2194\n",
      "Train Step: 2549, Loss: 0.1452\n",
      "Train Step: 2550, Loss: 0.2324\n",
      "Train Step: 2551, Loss: 0.1308\n",
      "Train Step: 2552, Loss: 0.0804\n",
      "Train Step: 2553, Loss: 0.2061\n",
      "Train Step: 2554, Loss: 0.1022\n",
      "Train Step: 2555, Loss: 0.1781\n",
      "Train Step: 2556, Loss: 0.1536\n",
      "Train Step: 2557, Loss: 0.2422\n",
      "Train Step: 2558, Loss: 0.1546\n",
      "Train Step: 2559, Loss: 0.1944\n",
      "Train Step: 2560, Loss: 0.1444\n",
      "Train Step: 2561, Loss: 0.1946\n",
      "Train Step: 2562, Loss: 0.1299\n",
      "Train Step: 2563, Loss: 0.1621\n",
      "Train Step: 2564, Loss: 0.1923\n",
      "Train Step: 2565, Loss: 0.2382\n",
      "Train Step: 2566, Loss: 0.0856\n",
      "Train Step: 2567, Loss: 0.1105\n",
      "Train Step: 2568, Loss: 0.0896\n",
      "Train Step: 2569, Loss: 0.1537\n",
      "Train Step: 2570, Loss: 0.1800\n",
      "Train Step: 2571, Loss: 0.1319\n",
      "Train Step: 2572, Loss: 0.1556\n",
      "Train Step: 2573, Loss: 0.0704\n",
      "Train Step: 2574, Loss: 0.1787\n",
      "Train Step: 2575, Loss: 0.1994\n",
      "Train Step: 2576, Loss: 0.1224\n",
      "Train Step: 2577, Loss: 0.2887\n",
      "Train Step: 2578, Loss: 0.1639\n",
      "Train Step: 2579, Loss: 0.2866\n",
      "Train Step: 2580, Loss: 0.1426\n",
      "Train Step: 2581, Loss: 0.1753\n",
      "Train Step: 2582, Loss: 0.3048\n",
      "Train Step: 2583, Loss: 0.0712\n",
      "Train Step: 2584, Loss: 0.1745\n",
      "Train Step: 2585, Loss: 0.1254\n",
      "Train Step: 2586, Loss: 0.1607\n",
      "Train Step: 2587, Loss: 0.2332\n",
      "Train Step: 2588, Loss: 0.0869\n",
      "Train Step: 2589, Loss: 0.1625\n",
      "Train Step: 2590, Loss: 0.2240\n",
      "Train Step: 2591, Loss: 0.1215\n",
      "Train Step: 2592, Loss: 0.2830\n",
      "Train Step: 2593, Loss: 0.1479\n",
      "Train Step: 2594, Loss: 0.2098\n",
      "Train Step: 2595, Loss: 0.2042\n",
      "Train Step: 2596, Loss: 0.1784\n",
      "Train Step: 2597, Loss: 0.2242\n",
      "Train Step: 2598, Loss: 0.1886\n",
      "Train Step: 2599, Loss: 0.2990\n",
      "Train Step: 2600, Loss: 0.1687\n",
      "Train Step: 2601, Loss: 0.1827\n",
      "Train Step: 2602, Loss: 0.3414\n",
      "Train Step: 2603, Loss: 0.1295\n",
      "Train Step: 2604, Loss: 0.1215\n",
      "Train Step: 2605, Loss: 0.1755\n",
      "Train Step: 2606, Loss: 0.2606\n",
      "Train Step: 2607, Loss: 0.1714\n",
      "Train Step: 2608, Loss: 0.3451\n",
      "Train Step: 2609, Loss: 0.2526\n",
      "Train Step: 2610, Loss: 0.3517\n",
      "Train Step: 2611, Loss: 0.3651\n",
      "Train Step: 2612, Loss: 0.1688\n",
      "Train Step: 2613, Loss: 0.1210\n",
      "Train Step: 2614, Loss: 0.1954\n",
      "Train Step: 2615, Loss: 0.0956\n",
      "Train Step: 2616, Loss: 0.1254\n",
      "Train Step: 2617, Loss: 0.1725\n",
      "Train Step: 2618, Loss: 0.2462\n",
      "Train Step: 2619, Loss: 0.1815\n",
      "Train Step: 2620, Loss: 0.2670\n",
      "Train Step: 2621, Loss: 0.1432\n",
      "Train Step: 2622, Loss: 0.0591\n",
      "Train Step: 2623, Loss: 0.1484\n",
      "Train Step: 2624, Loss: 0.1432\n",
      "Train Step: 2625, Loss: 0.2356\n",
      "Train Step: 2626, Loss: 0.2055\n",
      "Train Step: 2627, Loss: 0.1261\n",
      "Train Step: 2628, Loss: 0.1271\n",
      "Train Step: 2629, Loss: 0.2221\n",
      "Train Step: 2630, Loss: 0.0613\n",
      "Train Step: 2631, Loss: 0.1451\n",
      "Train Step: 2632, Loss: 0.3030\n",
      "Train Step: 2633, Loss: 0.1824\n",
      "Train Step: 2634, Loss: 0.1705\n",
      "Train Step: 2635, Loss: 0.1577\n",
      "Train Step: 2636, Loss: 0.1627\n",
      "Train Step: 2637, Loss: 0.2104\n",
      "Train Step: 2638, Loss: 0.1144\n",
      "Train Step: 2639, Loss: 0.1721\n",
      "Train Step: 2640, Loss: 0.2527\n",
      "Train Step: 2641, Loss: 0.2065\n",
      "Train Step: 2642, Loss: 0.1868\n",
      "Train Step: 2643, Loss: 0.1698\n",
      "Train Step: 2644, Loss: 0.2262\n",
      "Train Step: 2645, Loss: 0.1568\n",
      "Train Step: 2646, Loss: 0.1103\n",
      "Train Step: 2647, Loss: 0.1426\n",
      "Train Step: 2648, Loss: 0.1733\n",
      "Train Step: 2649, Loss: 0.1434\n",
      "Train Step: 2650, Loss: 0.2191\n",
      "Train Step: 2651, Loss: 0.1645\n",
      "Train Step: 2652, Loss: 0.1188\n",
      "Train Step: 2653, Loss: 0.2143\n",
      "Train Step: 2654, Loss: 0.2375\n",
      "Train Step: 2655, Loss: 0.1892\n",
      "Train Step: 2656, Loss: 0.1589\n",
      "Train Step: 2657, Loss: 0.2160\n",
      "Train Step: 2658, Loss: 0.1208\n",
      "Train Step: 2659, Loss: 0.2228\n",
      "Train Step: 2660, Loss: 0.0936\n",
      "Train Step: 2661, Loss: 0.2218\n",
      "Train Step: 2662, Loss: 0.1566\n",
      "Train Step: 2663, Loss: 0.1360\n",
      "Train Step: 2664, Loss: 0.1429\n",
      "Train Step: 2665, Loss: 0.1794\n",
      "Train Step: 2666, Loss: 0.3912\n",
      "Train Step: 2667, Loss: 0.2949\n",
      "Train Step: 2668, Loss: 0.1713\n",
      "Train Step: 2669, Loss: 0.1331\n",
      "Train Step: 2670, Loss: 0.1798\n",
      "Train Step: 2671, Loss: 0.1744\n",
      "Train Step: 2672, Loss: 0.1349\n",
      "Train Step: 2673, Loss: 0.1295\n",
      "Train Step: 2674, Loss: 0.1468\n",
      "Train Step: 2675, Loss: 0.1196\n",
      "Train Step: 2676, Loss: 0.1563\n",
      "Train Step: 2677, Loss: 0.0921\n",
      "Train Step: 2678, Loss: 0.1348\n",
      "Train Step: 2679, Loss: 0.0899\n",
      "Train Step: 2680, Loss: 0.0855\n",
      "Train Step: 2681, Loss: 0.1820\n",
      "Train Step: 2682, Loss: 0.1896\n",
      "Train Step: 2683, Loss: 0.1564\n",
      "Train Step: 2684, Loss: 0.1797\n",
      "Train Step: 2685, Loss: 0.1594\n",
      "Train Step: 2686, Loss: 0.3105\n",
      "Train Step: 2687, Loss: 0.2443\n",
      "Train Step: 2688, Loss: 0.1853\n",
      "Train Step: 2689, Loss: 0.1000\n",
      "Train Step: 2690, Loss: 0.1358\n",
      "Train Step: 2691, Loss: 0.1487\n",
      "Train Step: 2692, Loss: 0.1724\n",
      "Train Step: 2693, Loss: 0.1293\n",
      "Train Step: 2694, Loss: 0.0695\n",
      "Train Step: 2695, Loss: 0.1826\n",
      "Train Step: 2696, Loss: 0.2223\n",
      "Train Step: 2697, Loss: 0.2537\n",
      "Train Step: 2698, Loss: 0.1568\n",
      "Train Step: 2699, Loss: 0.2086\n",
      "Train Step: 2700, Loss: 0.2595\n",
      "Train Step: 2701, Loss: 0.1563\n",
      "Train Step: 2702, Loss: 0.2212\n",
      "Train Step: 2703, Loss: 0.2417\n",
      "Train Step: 2704, Loss: 0.4277\n",
      "Train Step: 2705, Loss: 0.1939\n",
      "Train Step: 2706, Loss: 0.0942\n",
      "Train Step: 2707, Loss: 0.3201\n",
      "Train Step: 2708, Loss: 0.2357\n",
      "Train Step: 2709, Loss: 0.1653\n",
      "Train Step: 2710, Loss: 0.1749\n",
      "Train Step: 2711, Loss: 0.1124\n",
      "Train Step: 2712, Loss: 0.1535\n",
      "Train Step: 2713, Loss: 0.2240\n",
      "Train Step: 2714, Loss: 0.3602\n",
      "Train Step: 2715, Loss: 0.1873\n",
      "Train Step: 2716, Loss: 0.0797\n",
      "Train Step: 2717, Loss: 0.2779\n",
      "Train Step: 2718, Loss: 0.1751\n",
      "Train Step: 2719, Loss: 0.1024\n",
      "Train Step: 2720, Loss: 0.1665\n",
      "Train Step: 2721, Loss: 0.2395\n",
      "Train Step: 2722, Loss: 0.1691\n",
      "Train Step: 2723, Loss: 0.1343\n",
      "Train Step: 2724, Loss: 0.1225\n",
      "Train Step: 2725, Loss: 0.1564\n",
      "Train Step: 2726, Loss: 0.1084\n",
      "Train Step: 2727, Loss: 0.1635\n",
      "Train Step: 2728, Loss: 0.1592\n",
      "Train Step: 2729, Loss: 0.1135\n",
      "Train Step: 2730, Loss: 0.1351\n",
      "Train Step: 2731, Loss: 0.2275\n",
      "Train Step: 2732, Loss: 0.1324\n",
      "Train Step: 2733, Loss: 0.1138\n",
      "Train Step: 2734, Loss: 0.1797\n",
      "Train Step: 2735, Loss: 0.2923\n",
      "Train Step: 2736, Loss: 0.0935\n",
      "Train Step: 2737, Loss: 0.4374\n",
      "Epoch [7/10], Loss: 0.1767\n",
      "Train Step: 2738, Loss: 0.1362\n",
      "Train Step: 2739, Loss: 0.1690\n",
      "Train Step: 2740, Loss: 0.1319\n",
      "Train Step: 2741, Loss: 0.2129\n",
      "Train Step: 2742, Loss: 0.0465\n",
      "Train Step: 2743, Loss: 0.1213\n",
      "Train Step: 2744, Loss: 0.0715\n",
      "Train Step: 2745, Loss: 0.1592\n",
      "Train Step: 2746, Loss: 0.0750\n",
      "Train Step: 2747, Loss: 0.1540\n",
      "Train Step: 2748, Loss: 0.1369\n",
      "Train Step: 2749, Loss: 0.1354\n",
      "Train Step: 2750, Loss: 0.1342\n",
      "Train Step: 2751, Loss: 0.1208\n",
      "Train Step: 2752, Loss: 0.0620\n",
      "Train Step: 2753, Loss: 0.1832\n",
      "Train Step: 2754, Loss: 0.1056\n",
      "Train Step: 2755, Loss: 0.1303\n",
      "Train Step: 2756, Loss: 0.0971\n",
      "Train Step: 2757, Loss: 0.1392\n",
      "Train Step: 2758, Loss: 0.0909\n",
      "Train Step: 2759, Loss: 0.1705\n",
      "Train Step: 2760, Loss: 0.0768\n",
      "Train Step: 2761, Loss: 0.1281\n",
      "Train Step: 2762, Loss: 0.1635\n",
      "Train Step: 2763, Loss: 0.0599\n",
      "Train Step: 2764, Loss: 0.1514\n",
      "Train Step: 2765, Loss: 0.0617\n",
      "Train Step: 2766, Loss: 0.1872\n",
      "Train Step: 2767, Loss: 0.1564\n",
      "Train Step: 2768, Loss: 0.0860\n",
      "Train Step: 2769, Loss: 0.0638\n",
      "Train Step: 2770, Loss: 0.1328\n",
      "Train Step: 2771, Loss: 0.1743\n",
      "Train Step: 2772, Loss: 0.1270\n",
      "Train Step: 2773, Loss: 0.1057\n",
      "Train Step: 2774, Loss: 0.1827\n",
      "Train Step: 2775, Loss: 0.1221\n",
      "Train Step: 2776, Loss: 0.1398\n",
      "Train Step: 2777, Loss: 0.0768\n",
      "Train Step: 2778, Loss: 0.0590\n",
      "Train Step: 2779, Loss: 0.1216\n",
      "Train Step: 2780, Loss: 0.1391\n",
      "Train Step: 2781, Loss: 0.0876\n",
      "Train Step: 2782, Loss: 0.1045\n",
      "Train Step: 2783, Loss: 0.1658\n",
      "Train Step: 2784, Loss: 0.1814\n",
      "Train Step: 2785, Loss: 0.1457\n",
      "Train Step: 2786, Loss: 0.0995\n",
      "Train Step: 2787, Loss: 0.1561\n",
      "Train Step: 2788, Loss: 0.1056\n",
      "Train Step: 2789, Loss: 0.0847\n",
      "Train Step: 2790, Loss: 0.1086\n",
      "Train Step: 2791, Loss: 0.0616\n",
      "Train Step: 2792, Loss: 0.2825\n",
      "Train Step: 2793, Loss: 0.1004\n",
      "Train Step: 2794, Loss: 0.1593\n",
      "Train Step: 2795, Loss: 0.1445\n",
      "Train Step: 2796, Loss: 0.1758\n",
      "Train Step: 2797, Loss: 0.0899\n",
      "Train Step: 2798, Loss: 0.1196\n",
      "Train Step: 2799, Loss: 0.1056\n",
      "Train Step: 2800, Loss: 0.1561\n",
      "Train Step: 2801, Loss: 0.1234\n",
      "Train Step: 2802, Loss: 0.0883\n",
      "Train Step: 2803, Loss: 0.1379\n",
      "Train Step: 2804, Loss: 0.0845\n",
      "Train Step: 2805, Loss: 0.0874\n",
      "Train Step: 2806, Loss: 0.2108\n",
      "Train Step: 2807, Loss: 0.1522\n",
      "Train Step: 2808, Loss: 0.1771\n",
      "Train Step: 2809, Loss: 0.0873\n",
      "Train Step: 2810, Loss: 0.0464\n",
      "Train Step: 2811, Loss: 0.0866\n",
      "Train Step: 2812, Loss: 0.0618\n",
      "Train Step: 2813, Loss: 0.0568\n",
      "Train Step: 2814, Loss: 0.1984\n",
      "Train Step: 2815, Loss: 0.2392\n",
      "Train Step: 2816, Loss: 0.0628\n",
      "Train Step: 2817, Loss: 0.0827\n",
      "Train Step: 2818, Loss: 0.0932\n",
      "Train Step: 2819, Loss: 0.1543\n",
      "Train Step: 2820, Loss: 0.2064\n",
      "Train Step: 2821, Loss: 0.1362\n",
      "Train Step: 2822, Loss: 0.1822\n",
      "Train Step: 2823, Loss: 0.1083\n",
      "Train Step: 2824, Loss: 0.1283\n",
      "Train Step: 2825, Loss: 0.0647\n",
      "Train Step: 2826, Loss: 0.0723\n",
      "Train Step: 2827, Loss: 0.1162\n",
      "Train Step: 2828, Loss: 0.0925\n",
      "Train Step: 2829, Loss: 0.1098\n",
      "Train Step: 2830, Loss: 0.0792\n",
      "Train Step: 2831, Loss: 0.1243\n",
      "Train Step: 2832, Loss: 0.1395\n",
      "Train Step: 2833, Loss: 0.0725\n",
      "Train Step: 2834, Loss: 0.2032\n",
      "Train Step: 2835, Loss: 0.0648\n",
      "Train Step: 2836, Loss: 0.0499\n",
      "Train Step: 2837, Loss: 0.1743\n",
      "Train Step: 2838, Loss: 0.2162\n",
      "Train Step: 2839, Loss: 0.0719\n",
      "Train Step: 2840, Loss: 0.0816\n",
      "Train Step: 2841, Loss: 0.0709\n",
      "Train Step: 2842, Loss: 0.0571\n",
      "Train Step: 2843, Loss: 0.0936\n",
      "Train Step: 2844, Loss: 0.1885\n",
      "Train Step: 2845, Loss: 0.1224\n",
      "Train Step: 2846, Loss: 0.2126\n",
      "Train Step: 2847, Loss: 0.1197\n",
      "Train Step: 2848, Loss: 0.1299\n",
      "Train Step: 2849, Loss: 0.0933\n",
      "Train Step: 2850, Loss: 0.1683\n",
      "Train Step: 2851, Loss: 0.0712\n",
      "Train Step: 2852, Loss: 0.1808\n",
      "Train Step: 2853, Loss: 0.1143\n",
      "Train Step: 2854, Loss: 0.1951\n",
      "Train Step: 2855, Loss: 0.2018\n",
      "Train Step: 2856, Loss: 0.1634\n",
      "Train Step: 2857, Loss: 0.0464\n",
      "Train Step: 2858, Loss: 0.1377\n",
      "Train Step: 2859, Loss: 0.1169\n",
      "Train Step: 2860, Loss: 0.1096\n",
      "Train Step: 2861, Loss: 0.1276\n",
      "Train Step: 2862, Loss: 0.1131\n",
      "Train Step: 2863, Loss: 0.1636\n",
      "Train Step: 2864, Loss: 0.0705\n",
      "Train Step: 2865, Loss: 0.1194\n",
      "Train Step: 2866, Loss: 0.0889\n",
      "Train Step: 2867, Loss: 0.1840\n",
      "Train Step: 2868, Loss: 0.1044\n",
      "Train Step: 2869, Loss: 0.1817\n",
      "Train Step: 2870, Loss: 0.0481\n",
      "Train Step: 2871, Loss: 0.0665\n",
      "Train Step: 2872, Loss: 0.0598\n",
      "Train Step: 2873, Loss: 0.1207\n",
      "Train Step: 2874, Loss: 0.2338\n",
      "Train Step: 2875, Loss: 0.0778\n",
      "Train Step: 2876, Loss: 0.1434\n",
      "Train Step: 2877, Loss: 0.1112\n",
      "Train Step: 2878, Loss: 0.0953\n",
      "Train Step: 2879, Loss: 0.0586\n",
      "Train Step: 2880, Loss: 0.0755\n",
      "Train Step: 2881, Loss: 0.1541\n",
      "Train Step: 2882, Loss: 0.1521\n",
      "Train Step: 2883, Loss: 0.2023\n",
      "Train Step: 2884, Loss: 0.0719\n",
      "Train Step: 2885, Loss: 0.0776\n",
      "Train Step: 2886, Loss: 0.0487\n",
      "Train Step: 2887, Loss: 0.1287\n",
      "Train Step: 2888, Loss: 0.0838\n",
      "Train Step: 2889, Loss: 0.1109\n",
      "Train Step: 2890, Loss: 0.0842\n",
      "Train Step: 2891, Loss: 0.1010\n",
      "Train Step: 2892, Loss: 0.2747\n",
      "Train Step: 2893, Loss: 0.0861\n",
      "Train Step: 2894, Loss: 0.0525\n",
      "Train Step: 2895, Loss: 0.1344\n",
      "Train Step: 2896, Loss: 0.1536\n",
      "Train Step: 2897, Loss: 0.2603\n",
      "Train Step: 2898, Loss: 0.0890\n",
      "Train Step: 2899, Loss: 0.1740\n",
      "Train Step: 2900, Loss: 0.1180\n",
      "Train Step: 2901, Loss: 0.1341\n",
      "Train Step: 2902, Loss: 0.1058\n",
      "Train Step: 2903, Loss: 0.1301\n",
      "Train Step: 2904, Loss: 0.1792\n",
      "Train Step: 2905, Loss: 0.0755\n",
      "Train Step: 2906, Loss: 0.1764\n",
      "Train Step: 2907, Loss: 0.1564\n",
      "Train Step: 2908, Loss: 0.0946\n",
      "Train Step: 2909, Loss: 0.1231\n",
      "Train Step: 2910, Loss: 0.0585\n",
      "Train Step: 2911, Loss: 0.1688\n",
      "Train Step: 2912, Loss: 0.0719\n",
      "Train Step: 2913, Loss: 0.1603\n",
      "Train Step: 2914, Loss: 0.0901\n",
      "Train Step: 2915, Loss: 0.0889\n",
      "Train Step: 2916, Loss: 0.1643\n",
      "Train Step: 2917, Loss: 0.1283\n",
      "Train Step: 2918, Loss: 0.2007\n",
      "Train Step: 2919, Loss: 0.0999\n",
      "Train Step: 2920, Loss: 0.1299\n",
      "Train Step: 2921, Loss: 0.0751\n",
      "Train Step: 2922, Loss: 0.1777\n",
      "Train Step: 2923, Loss: 0.0804\n",
      "Train Step: 2924, Loss: 0.2409\n",
      "Train Step: 2925, Loss: 0.0975\n",
      "Train Step: 2926, Loss: 0.2354\n",
      "Train Step: 2927, Loss: 0.0477\n",
      "Train Step: 2928, Loss: 0.1505\n",
      "Train Step: 2929, Loss: 0.0372\n",
      "Train Step: 2930, Loss: 0.0729\n",
      "Train Step: 2931, Loss: 0.1569\n",
      "Train Step: 2932, Loss: 0.0689\n",
      "Train Step: 2933, Loss: 0.0866\n",
      "Train Step: 2934, Loss: 0.0544\n",
      "Train Step: 2935, Loss: 0.1864\n",
      "Train Step: 2936, Loss: 0.1572\n",
      "Train Step: 2937, Loss: 0.1873\n",
      "Train Step: 2938, Loss: 0.0522\n",
      "Train Step: 2939, Loss: 0.2804\n",
      "Train Step: 2940, Loss: 0.0867\n",
      "Train Step: 2941, Loss: 0.0926\n",
      "Train Step: 2942, Loss: 0.1452\n",
      "Train Step: 2943, Loss: 0.0967\n",
      "Train Step: 2944, Loss: 0.0501\n",
      "Train Step: 2945, Loss: 0.1155\n",
      "Train Step: 2946, Loss: 0.2373\n",
      "Train Step: 2947, Loss: 0.0878\n",
      "Train Step: 2948, Loss: 0.1800\n",
      "Train Step: 2949, Loss: 0.1125\n",
      "Train Step: 2950, Loss: 0.1569\n",
      "Train Step: 2951, Loss: 0.1950\n",
      "Train Step: 2952, Loss: 0.1294\n",
      "Train Step: 2953, Loss: 0.0744\n",
      "Train Step: 2954, Loss: 0.0670\n",
      "Train Step: 2955, Loss: 0.1116\n",
      "Train Step: 2956, Loss: 0.3860\n",
      "Train Step: 2957, Loss: 0.1603\n",
      "Train Step: 2958, Loss: 0.1182\n",
      "Train Step: 2959, Loss: 0.2962\n",
      "Train Step: 2960, Loss: 0.1470\n",
      "Train Step: 2961, Loss: 0.2048\n",
      "Train Step: 2962, Loss: 0.1406\n",
      "Train Step: 2963, Loss: 0.1115\n",
      "Train Step: 2964, Loss: 0.1585\n",
      "Train Step: 2965, Loss: 0.1954\n",
      "Train Step: 2966, Loss: 0.0765\n",
      "Train Step: 2967, Loss: 0.1799\n",
      "Train Step: 2968, Loss: 0.1659\n",
      "Train Step: 2969, Loss: 0.0607\n",
      "Train Step: 2970, Loss: 0.1843\n",
      "Train Step: 2971, Loss: 0.1006\n",
      "Train Step: 2972, Loss: 0.1625\n",
      "Train Step: 2973, Loss: 0.1399\n",
      "Train Step: 2974, Loss: 0.2353\n",
      "Train Step: 2975, Loss: 0.0885\n",
      "Train Step: 2976, Loss: 0.1136\n",
      "Train Step: 2977, Loss: 0.1185\n",
      "Train Step: 2978, Loss: 0.1167\n",
      "Train Step: 2979, Loss: 0.2222\n",
      "Train Step: 2980, Loss: 0.1082\n",
      "Train Step: 2981, Loss: 0.1440\n",
      "Train Step: 2982, Loss: 0.1693\n",
      "Train Step: 2983, Loss: 0.1292\n",
      "Train Step: 2984, Loss: 0.1601\n",
      "Train Step: 2985, Loss: 0.0934\n",
      "Train Step: 2986, Loss: 0.1257\n",
      "Train Step: 2987, Loss: 0.1141\n",
      "Train Step: 2988, Loss: 0.0425\n",
      "Train Step: 2989, Loss: 0.0523\n",
      "Train Step: 2990, Loss: 0.0807\n",
      "Train Step: 2991, Loss: 0.0523\n",
      "Train Step: 2992, Loss: 0.1668\n",
      "Train Step: 2993, Loss: 0.1210\n",
      "Train Step: 2994, Loss: 0.0617\n",
      "Train Step: 2995, Loss: 0.1438\n",
      "Train Step: 2996, Loss: 0.0818\n",
      "Train Step: 2997, Loss: 0.1885\n",
      "Train Step: 2998, Loss: 0.0756\n",
      "Train Step: 2999, Loss: 0.1961\n",
      "Train Step: 3000, Loss: 0.1034\n",
      "Train Step: 3001, Loss: 0.1126\n",
      "Train Step: 3002, Loss: 0.1440\n",
      "Train Step: 3003, Loss: 0.1015\n",
      "Train Step: 3004, Loss: 0.1190\n",
      "Train Step: 3005, Loss: 0.0836\n",
      "Train Step: 3006, Loss: 0.0449\n",
      "Train Step: 3007, Loss: 0.1229\n",
      "Train Step: 3008, Loss: 0.1142\n",
      "Train Step: 3009, Loss: 0.2787\n",
      "Train Step: 3010, Loss: 0.0739\n",
      "Train Step: 3011, Loss: 0.1018\n",
      "Train Step: 3012, Loss: 0.1471\n",
      "Train Step: 3013, Loss: 0.1093\n",
      "Train Step: 3014, Loss: 0.0903\n",
      "Train Step: 3015, Loss: 0.0789\n",
      "Train Step: 3016, Loss: 0.0912\n",
      "Train Step: 3017, Loss: 0.1238\n",
      "Train Step: 3018, Loss: 0.1162\n",
      "Train Step: 3019, Loss: 0.2184\n",
      "Train Step: 3020, Loss: 0.1316\n",
      "Train Step: 3021, Loss: 0.0998\n",
      "Train Step: 3022, Loss: 0.0869\n",
      "Train Step: 3023, Loss: 0.2205\n",
      "Train Step: 3024, Loss: 0.2019\n",
      "Train Step: 3025, Loss: 0.2163\n",
      "Train Step: 3026, Loss: 0.0816\n",
      "Train Step: 3027, Loss: 0.0621\n",
      "Train Step: 3028, Loss: 0.0735\n",
      "Train Step: 3029, Loss: 0.1016\n",
      "Train Step: 3030, Loss: 0.1740\n",
      "Train Step: 3031, Loss: 0.0884\n",
      "Train Step: 3032, Loss: 0.0839\n",
      "Train Step: 3033, Loss: 0.0566\n",
      "Train Step: 3034, Loss: 0.1152\n",
      "Train Step: 3035, Loss: 0.0888\n",
      "Train Step: 3036, Loss: 0.0917\n",
      "Train Step: 3037, Loss: 0.1838\n",
      "Train Step: 3038, Loss: 0.0809\n",
      "Train Step: 3039, Loss: 0.0841\n",
      "Train Step: 3040, Loss: 0.1838\n",
      "Train Step: 3041, Loss: 0.1261\n",
      "Train Step: 3042, Loss: 0.0840\n",
      "Train Step: 3043, Loss: 0.1820\n",
      "Train Step: 3044, Loss: 0.1346\n",
      "Train Step: 3045, Loss: 0.0710\n",
      "Train Step: 3046, Loss: 0.0710\n",
      "Train Step: 3047, Loss: 0.0844\n",
      "Train Step: 3048, Loss: 0.0554\n",
      "Train Step: 3049, Loss: 0.0935\n",
      "Train Step: 3050, Loss: 0.1448\n",
      "Train Step: 3051, Loss: 0.1001\n",
      "Train Step: 3052, Loss: 0.2210\n",
      "Train Step: 3053, Loss: 0.2584\n",
      "Train Step: 3054, Loss: 0.1343\n",
      "Train Step: 3055, Loss: 0.1451\n",
      "Train Step: 3056, Loss: 0.1105\n",
      "Train Step: 3057, Loss: 0.1366\n",
      "Train Step: 3058, Loss: 0.0815\n",
      "Train Step: 3059, Loss: 0.1663\n",
      "Train Step: 3060, Loss: 0.1005\n",
      "Train Step: 3061, Loss: 0.2249\n",
      "Train Step: 3062, Loss: 0.2942\n",
      "Train Step: 3063, Loss: 0.1483\n",
      "Train Step: 3064, Loss: 0.0866\n",
      "Train Step: 3065, Loss: 0.1224\n",
      "Train Step: 3066, Loss: 0.1498\n",
      "Train Step: 3067, Loss: 0.0547\n",
      "Train Step: 3068, Loss: 0.1176\n",
      "Train Step: 3069, Loss: 0.1042\n",
      "Train Step: 3070, Loss: 0.1463\n",
      "Train Step: 3071, Loss: 0.0785\n",
      "Train Step: 3072, Loss: 0.1149\n",
      "Train Step: 3073, Loss: 0.0833\n",
      "Train Step: 3074, Loss: 0.1486\n",
      "Train Step: 3075, Loss: 0.1322\n",
      "Train Step: 3076, Loss: 0.0782\n",
      "Train Step: 3077, Loss: 0.0840\n",
      "Train Step: 3078, Loss: 0.1166\n",
      "Train Step: 3079, Loss: 0.1235\n",
      "Train Step: 3080, Loss: 0.0652\n",
      "Train Step: 3081, Loss: 0.1185\n",
      "Train Step: 3082, Loss: 0.0662\n",
      "Train Step: 3083, Loss: 0.1242\n",
      "Train Step: 3084, Loss: 0.1120\n",
      "Train Step: 3085, Loss: 0.2310\n",
      "Train Step: 3086, Loss: 0.1921\n",
      "Train Step: 3087, Loss: 0.1916\n",
      "Train Step: 3088, Loss: 0.2818\n",
      "Train Step: 3089, Loss: 0.1750\n",
      "Train Step: 3090, Loss: 0.0942\n",
      "Train Step: 3091, Loss: 0.1327\n",
      "Train Step: 3092, Loss: 0.1769\n",
      "Train Step: 3093, Loss: 0.2098\n",
      "Train Step: 3094, Loss: 0.0553\n",
      "Train Step: 3095, Loss: 0.2242\n",
      "Train Step: 3096, Loss: 0.1455\n",
      "Train Step: 3097, Loss: 0.0797\n",
      "Train Step: 3098, Loss: 0.1523\n",
      "Train Step: 3099, Loss: 0.1133\n",
      "Train Step: 3100, Loss: 0.1286\n",
      "Train Step: 3101, Loss: 0.1541\n",
      "Train Step: 3102, Loss: 0.2136\n",
      "Train Step: 3103, Loss: 0.0986\n",
      "Train Step: 3104, Loss: 0.1669\n",
      "Train Step: 3105, Loss: 0.0998\n",
      "Train Step: 3106, Loss: 0.0817\n",
      "Train Step: 3107, Loss: 0.0731\n",
      "Train Step: 3108, Loss: 0.1220\n",
      "Train Step: 3109, Loss: 0.1178\n",
      "Train Step: 3110, Loss: 0.2663\n",
      "Train Step: 3111, Loss: 0.0718\n",
      "Train Step: 3112, Loss: 0.2504\n",
      "Train Step: 3113, Loss: 0.0712\n",
      "Train Step: 3114, Loss: 0.0772\n",
      "Train Step: 3115, Loss: 0.2039\n",
      "Train Step: 3116, Loss: 0.1706\n",
      "Train Step: 3117, Loss: 0.2623\n",
      "Train Step: 3118, Loss: 0.1905\n",
      "Train Step: 3119, Loss: 0.0872\n",
      "Train Step: 3120, Loss: 0.1220\n",
      "Train Step: 3121, Loss: 0.1785\n",
      "Train Step: 3122, Loss: 0.0345\n",
      "Train Step: 3123, Loss: 0.0823\n",
      "Train Step: 3124, Loss: 0.0943\n",
      "Train Step: 3125, Loss: 0.1951\n",
      "Train Step: 3126, Loss: 0.0824\n",
      "Train Step: 3127, Loss: 0.1618\n",
      "Train Step: 3128, Loss: 0.1241\n",
      "Epoch [8/10], Loss: 0.1276\n",
      "Train Step: 3129, Loss: 0.0681\n",
      "Train Step: 3130, Loss: 0.0667\n",
      "Train Step: 3131, Loss: 0.1071\n",
      "Train Step: 3132, Loss: 0.2096\n",
      "Train Step: 3133, Loss: 0.0689\n",
      "Train Step: 3134, Loss: 0.0559\n",
      "Train Step: 3135, Loss: 0.0292\n",
      "Train Step: 3136, Loss: 0.0619\n",
      "Train Step: 3137, Loss: 0.0423\n",
      "Train Step: 3138, Loss: 0.0661\n",
      "Train Step: 3139, Loss: 0.0516\n",
      "Train Step: 3140, Loss: 0.1185\n",
      "Train Step: 3141, Loss: 0.0636\n",
      "Train Step: 3142, Loss: 0.0564\n",
      "Train Step: 3143, Loss: 0.1082\n",
      "Train Step: 3144, Loss: 0.1003\n",
      "Train Step: 3145, Loss: 0.0527\n",
      "Train Step: 3146, Loss: 0.0357\n",
      "Train Step: 3147, Loss: 0.1076\n",
      "Train Step: 3148, Loss: 0.0651\n",
      "Train Step: 3149, Loss: 0.1310\n",
      "Train Step: 3150, Loss: 0.1785\n",
      "Train Step: 3151, Loss: 0.0626\n",
      "Train Step: 3152, Loss: 0.0529\n",
      "Train Step: 3153, Loss: 0.0673\n",
      "Train Step: 3154, Loss: 0.0688\n",
      "Train Step: 3155, Loss: 0.1881\n",
      "Train Step: 3156, Loss: 0.1032\n",
      "Train Step: 3157, Loss: 0.1362\n",
      "Train Step: 3158, Loss: 0.0996\n",
      "Train Step: 3159, Loss: 0.0589\n",
      "Train Step: 3160, Loss: 0.0825\n",
      "Train Step: 3161, Loss: 0.0926\n",
      "Train Step: 3162, Loss: 0.0516\n",
      "Train Step: 3163, Loss: 0.0528\n",
      "Train Step: 3164, Loss: 0.0488\n",
      "Train Step: 3165, Loss: 0.1347\n",
      "Train Step: 3166, Loss: 0.1020\n",
      "Train Step: 3167, Loss: 0.1920\n",
      "Train Step: 3168, Loss: 0.0874\n",
      "Train Step: 3169, Loss: 0.0836\n",
      "Train Step: 3170, Loss: 0.0862\n",
      "Train Step: 3171, Loss: 0.0502\n",
      "Train Step: 3172, Loss: 0.1982\n",
      "Train Step: 3173, Loss: 0.1337\n",
      "Train Step: 3174, Loss: 0.0634\n",
      "Train Step: 3175, Loss: 0.0676\n",
      "Train Step: 3176, Loss: 0.0872\n",
      "Train Step: 3177, Loss: 0.0914\n",
      "Train Step: 3178, Loss: 0.0479\n",
      "Train Step: 3179, Loss: 0.1339\n",
      "Train Step: 3180, Loss: 0.0782\n",
      "Train Step: 3181, Loss: 0.0817\n",
      "Train Step: 3182, Loss: 0.0551\n",
      "Train Step: 3183, Loss: 0.0813\n",
      "Train Step: 3184, Loss: 0.1422\n",
      "Train Step: 3185, Loss: 0.0650\n",
      "Train Step: 3186, Loss: 0.0403\n",
      "Train Step: 3187, Loss: 0.1220\n",
      "Train Step: 3188, Loss: 0.1344\n",
      "Train Step: 3189, Loss: 0.1220\n",
      "Train Step: 3190, Loss: 0.1074\n",
      "Train Step: 3191, Loss: 0.1103\n",
      "Train Step: 3192, Loss: 0.0878\n",
      "Train Step: 3193, Loss: 0.0490\n",
      "Train Step: 3194, Loss: 0.0547\n",
      "Train Step: 3195, Loss: 0.1359\n",
      "Train Step: 3196, Loss: 0.0752\n",
      "Train Step: 3197, Loss: 0.0494\n",
      "Train Step: 3198, Loss: 0.1111\n",
      "Train Step: 3199, Loss: 0.1233\n",
      "Train Step: 3200, Loss: 0.0815\n",
      "Train Step: 3201, Loss: 0.0987\n",
      "Train Step: 3202, Loss: 0.0665\n",
      "Train Step: 3203, Loss: 0.0556\n",
      "Train Step: 3204, Loss: 0.1031\n",
      "Train Step: 3205, Loss: 0.0807\n",
      "Train Step: 3206, Loss: 0.0942\n",
      "Train Step: 3207, Loss: 0.1296\n",
      "Train Step: 3208, Loss: 0.0292\n",
      "Train Step: 3209, Loss: 0.0955\n",
      "Train Step: 3210, Loss: 0.0549\n",
      "Train Step: 3211, Loss: 0.0587\n",
      "Train Step: 3212, Loss: 0.0676\n",
      "Train Step: 3213, Loss: 0.0358\n",
      "Train Step: 3214, Loss: 0.0764\n",
      "Train Step: 3215, Loss: 0.1086\n",
      "Train Step: 3216, Loss: 0.0942\n",
      "Train Step: 3217, Loss: 0.1401\n",
      "Train Step: 3218, Loss: 0.0859\n",
      "Train Step: 3219, Loss: 0.1087\n",
      "Train Step: 3220, Loss: 0.0806\n",
      "Train Step: 3221, Loss: 0.0592\n",
      "Train Step: 3222, Loss: 0.1526\n",
      "Train Step: 3223, Loss: 0.0669\n",
      "Train Step: 3224, Loss: 0.0950\n",
      "Train Step: 3225, Loss: 0.0800\n",
      "Train Step: 3226, Loss: 0.1153\n",
      "Train Step: 3227, Loss: 0.0675\n",
      "Train Step: 3228, Loss: 0.0671\n",
      "Train Step: 3229, Loss: 0.0934\n",
      "Train Step: 3230, Loss: 0.1221\n",
      "Train Step: 3231, Loss: 0.0282\n",
      "Train Step: 3232, Loss: 0.1244\n",
      "Train Step: 3233, Loss: 0.1248\n",
      "Train Step: 3234, Loss: 0.0368\n",
      "Train Step: 3235, Loss: 0.0611\n",
      "Train Step: 3236, Loss: 0.1326\n",
      "Train Step: 3237, Loss: 0.0789\n",
      "Train Step: 3238, Loss: 0.0313\n",
      "Train Step: 3239, Loss: 0.0212\n",
      "Train Step: 3240, Loss: 0.0806\n",
      "Train Step: 3241, Loss: 0.0611\n",
      "Train Step: 3242, Loss: 0.0381\n",
      "Train Step: 3243, Loss: 0.0663\n",
      "Train Step: 3244, Loss: 0.0904\n",
      "Train Step: 3245, Loss: 0.0411\n",
      "Train Step: 3246, Loss: 0.0764\n",
      "Train Step: 3247, Loss: 0.0637\n",
      "Train Step: 3248, Loss: 0.0464\n",
      "Train Step: 3249, Loss: 0.0750\n",
      "Train Step: 3250, Loss: 0.0949\n",
      "Train Step: 3251, Loss: 0.0396\n",
      "Train Step: 3252, Loss: 0.0873\n",
      "Train Step: 3253, Loss: 0.1100\n",
      "Train Step: 3254, Loss: 0.0885\n",
      "Train Step: 3255, Loss: 0.0517\n",
      "Train Step: 3256, Loss: 0.1765\n",
      "Train Step: 3257, Loss: 0.0987\n",
      "Train Step: 3258, Loss: 0.0876\n",
      "Train Step: 3259, Loss: 0.0664\n",
      "Train Step: 3260, Loss: 0.0707\n",
      "Train Step: 3261, Loss: 0.0657\n",
      "Train Step: 3262, Loss: 0.0793\n",
      "Train Step: 3263, Loss: 0.1021\n",
      "Train Step: 3264, Loss: 0.0747\n",
      "Train Step: 3265, Loss: 0.0606\n",
      "Train Step: 3266, Loss: 0.0612\n",
      "Train Step: 3267, Loss: 0.0438\n",
      "Train Step: 3268, Loss: 0.0669\n",
      "Train Step: 3269, Loss: 0.0263\n",
      "Train Step: 3270, Loss: 0.0586\n",
      "Train Step: 3271, Loss: 0.1046\n",
      "Train Step: 3272, Loss: 0.0789\n",
      "Train Step: 3273, Loss: 0.0786\n",
      "Train Step: 3274, Loss: 0.0654\n",
      "Train Step: 3275, Loss: 0.0740\n",
      "Train Step: 3276, Loss: 0.0862\n",
      "Train Step: 3277, Loss: 0.1482\n",
      "Train Step: 3278, Loss: 0.0223\n",
      "Train Step: 3279, Loss: 0.1145\n",
      "Train Step: 3280, Loss: 0.0501\n",
      "Train Step: 3281, Loss: 0.2026\n",
      "Train Step: 3282, Loss: 0.0571\n",
      "Train Step: 3283, Loss: 0.0258\n",
      "Train Step: 3284, Loss: 0.0563\n",
      "Train Step: 3285, Loss: 0.0456\n",
      "Train Step: 3286, Loss: 0.0568\n",
      "Train Step: 3287, Loss: 0.0665\n",
      "Train Step: 3288, Loss: 0.0569\n",
      "Train Step: 3289, Loss: 0.1147\n",
      "Train Step: 3290, Loss: 0.0975\n",
      "Train Step: 3291, Loss: 0.0504\n",
      "Train Step: 3292, Loss: 0.1692\n",
      "Train Step: 3293, Loss: 0.1154\n",
      "Train Step: 3294, Loss: 0.0946\n",
      "Train Step: 3295, Loss: 0.1439\n",
      "Train Step: 3296, Loss: 0.0382\n",
      "Train Step: 3297, Loss: 0.0295\n",
      "Train Step: 3298, Loss: 0.0825\n",
      "Train Step: 3299, Loss: 0.1249\n",
      "Train Step: 3300, Loss: 0.1202\n",
      "Train Step: 3301, Loss: 0.1574\n",
      "Train Step: 3302, Loss: 0.1526\n",
      "Train Step: 3303, Loss: 0.0688\n",
      "Train Step: 3304, Loss: 0.0743\n",
      "Train Step: 3305, Loss: 0.0972\n",
      "Train Step: 3306, Loss: 0.0915\n",
      "Train Step: 3307, Loss: 0.0536\n",
      "Train Step: 3308, Loss: 0.0717\n",
      "Train Step: 3309, Loss: 0.1544\n",
      "Train Step: 3310, Loss: 0.0599\n",
      "Train Step: 3311, Loss: 0.1109\n",
      "Train Step: 3312, Loss: 0.1307\n",
      "Train Step: 3313, Loss: 0.0833\n",
      "Train Step: 3314, Loss: 0.0778\n",
      "Train Step: 3315, Loss: 0.1377\n",
      "Train Step: 3316, Loss: 0.0896\n",
      "Train Step: 3317, Loss: 0.1356\n",
      "Train Step: 3318, Loss: 0.0651\n",
      "Train Step: 3319, Loss: 0.0597\n",
      "Train Step: 3320, Loss: 0.0971\n",
      "Train Step: 3321, Loss: 0.0293\n",
      "Train Step: 3322, Loss: 0.0625\n",
      "Train Step: 3323, Loss: 0.1185\n",
      "Train Step: 3324, Loss: 0.0792\n",
      "Train Step: 3325, Loss: 0.2383\n",
      "Train Step: 3326, Loss: 0.1268\n",
      "Train Step: 3327, Loss: 0.0563\n",
      "Train Step: 3328, Loss: 0.0614\n",
      "Train Step: 3329, Loss: 0.0522\n",
      "Train Step: 3330, Loss: 0.1553\n",
      "Train Step: 3331, Loss: 0.1020\n",
      "Train Step: 3332, Loss: 0.0718\n",
      "Train Step: 3333, Loss: 0.0609\n",
      "Train Step: 3334, Loss: 0.1426\n",
      "Train Step: 3335, Loss: 0.0384\n",
      "Train Step: 3336, Loss: 0.1278\n",
      "Train Step: 3337, Loss: 0.1951\n",
      "Train Step: 3338, Loss: 0.1035\n",
      "Train Step: 3339, Loss: 0.0777\n",
      "Train Step: 3340, Loss: 0.1112\n",
      "Train Step: 3341, Loss: 0.0427\n",
      "Train Step: 3342, Loss: 0.1269\n",
      "Train Step: 3343, Loss: 0.1261\n",
      "Train Step: 3344, Loss: 0.1199\n",
      "Train Step: 3345, Loss: 0.0333\n",
      "Train Step: 3346, Loss: 0.0673\n",
      "Train Step: 3347, Loss: 0.1118\n",
      "Train Step: 3348, Loss: 0.1619\n",
      "Train Step: 3349, Loss: 0.1002\n",
      "Train Step: 3350, Loss: 0.1233\n",
      "Train Step: 3351, Loss: 0.0488\n",
      "Train Step: 3352, Loss: 0.0474\n",
      "Train Step: 3353, Loss: 0.1192\n",
      "Train Step: 3354, Loss: 0.1691\n",
      "Train Step: 3355, Loss: 0.1153\n",
      "Train Step: 3356, Loss: 0.0462\n",
      "Train Step: 3357, Loss: 0.1822\n",
      "Train Step: 3358, Loss: 0.0475\n",
      "Train Step: 3359, Loss: 0.0647\n",
      "Train Step: 3360, Loss: 0.2108\n",
      "Train Step: 3361, Loss: 0.0562\n",
      "Train Step: 3362, Loss: 0.0383\n",
      "Train Step: 3363, Loss: 0.0537\n",
      "Train Step: 3364, Loss: 0.1393\n",
      "Train Step: 3365, Loss: 0.1835\n",
      "Train Step: 3366, Loss: 0.0439\n",
      "Train Step: 3367, Loss: 0.1202\n",
      "Train Step: 3368, Loss: 0.0469\n",
      "Train Step: 3369, Loss: 0.2239\n",
      "Train Step: 3370, Loss: 0.0924\n",
      "Train Step: 3371, Loss: 0.0799\n",
      "Train Step: 3372, Loss: 0.0321\n",
      "Train Step: 3373, Loss: 0.2109\n",
      "Train Step: 3374, Loss: 0.1383\n",
      "Train Step: 3375, Loss: 0.1427\n",
      "Train Step: 3376, Loss: 0.1434\n",
      "Train Step: 3377, Loss: 0.1143\n",
      "Train Step: 3378, Loss: 0.1346\n",
      "Train Step: 3379, Loss: 0.0845\n",
      "Train Step: 3380, Loss: 0.1078\n",
      "Train Step: 3381, Loss: 0.1132\n",
      "Train Step: 3382, Loss: 0.0538\n",
      "Train Step: 3383, Loss: 0.1098\n",
      "Train Step: 3384, Loss: 0.0592\n",
      "Train Step: 3385, Loss: 0.0925\n",
      "Train Step: 3386, Loss: 0.0604\n",
      "Train Step: 3387, Loss: 0.0463\n",
      "Train Step: 3388, Loss: 0.0735\n",
      "Train Step: 3389, Loss: 0.1340\n",
      "Train Step: 3390, Loss: 0.0472\n",
      "Train Step: 3391, Loss: 0.0923\n",
      "Train Step: 3392, Loss: 0.0737\n",
      "Train Step: 3393, Loss: 0.0592\n",
      "Train Step: 3394, Loss: 0.2091\n",
      "Train Step: 3395, Loss: 0.0368\n",
      "Train Step: 3396, Loss: 0.0774\n",
      "Train Step: 3397, Loss: 0.1648\n",
      "Train Step: 3398, Loss: 0.0401\n",
      "Train Step: 3399, Loss: 0.0779\n",
      "Train Step: 3400, Loss: 0.1000\n",
      "Train Step: 3401, Loss: 0.0709\n",
      "Train Step: 3402, Loss: 0.0499\n",
      "Train Step: 3403, Loss: 0.1264\n",
      "Train Step: 3404, Loss: 0.0997\n",
      "Train Step: 3405, Loss: 0.1528\n",
      "Train Step: 3406, Loss: 0.1003\n",
      "Train Step: 3407, Loss: 0.0648\n",
      "Train Step: 3408, Loss: 0.1186\n",
      "Train Step: 3409, Loss: 0.0660\n",
      "Train Step: 3410, Loss: 0.1925\n",
      "Train Step: 3411, Loss: 0.1270\n",
      "Train Step: 3412, Loss: 0.1395\n",
      "Train Step: 3413, Loss: 0.0377\n",
      "Train Step: 3414, Loss: 0.0431\n",
      "Train Step: 3415, Loss: 0.0675\n",
      "Train Step: 3416, Loss: 0.1373\n",
      "Train Step: 3417, Loss: 0.1465\n",
      "Train Step: 3418, Loss: 0.0803\n",
      "Train Step: 3419, Loss: 0.0894\n",
      "Train Step: 3420, Loss: 0.0791\n",
      "Train Step: 3421, Loss: 0.0372\n",
      "Train Step: 3422, Loss: 0.0865\n",
      "Train Step: 3423, Loss: 0.1827\n",
      "Train Step: 3424, Loss: 0.0432\n",
      "Train Step: 3425, Loss: 0.1909\n",
      "Train Step: 3426, Loss: 0.1258\n",
      "Train Step: 3427, Loss: 0.0411\n",
      "Train Step: 3428, Loss: 0.0891\n",
      "Train Step: 3429, Loss: 0.0891\n",
      "Train Step: 3430, Loss: 0.1679\n",
      "Train Step: 3431, Loss: 0.1505\n",
      "Train Step: 3432, Loss: 0.0253\n",
      "Train Step: 3433, Loss: 0.2537\n",
      "Train Step: 3434, Loss: 0.0150\n",
      "Train Step: 3435, Loss: 0.0809\n",
      "Train Step: 3436, Loss: 0.0473\n",
      "Train Step: 3437, Loss: 0.0157\n",
      "Train Step: 3438, Loss: 0.1703\n",
      "Train Step: 3439, Loss: 0.0726\n",
      "Train Step: 3440, Loss: 0.0572\n",
      "Train Step: 3441, Loss: 0.0210\n",
      "Train Step: 3442, Loss: 0.0425\n",
      "Train Step: 3443, Loss: 0.1140\n",
      "Train Step: 3444, Loss: 0.1502\n",
      "Train Step: 3445, Loss: 0.0518\n",
      "Train Step: 3446, Loss: 0.1410\n",
      "Train Step: 3447, Loss: 0.0930\n",
      "Train Step: 3448, Loss: 0.0585\n",
      "Train Step: 3449, Loss: 0.0601\n",
      "Train Step: 3450, Loss: 0.1123\n",
      "Train Step: 3451, Loss: 0.1253\n",
      "Train Step: 3452, Loss: 0.0321\n",
      "Train Step: 3453, Loss: 0.0293\n",
      "Train Step: 3454, Loss: 0.0904\n",
      "Train Step: 3455, Loss: 0.1335\n",
      "Train Step: 3456, Loss: 0.0562\n",
      "Train Step: 3457, Loss: 0.0992\n",
      "Train Step: 3458, Loss: 0.0576\n",
      "Train Step: 3459, Loss: 0.1273\n",
      "Train Step: 3460, Loss: 0.1023\n",
      "Train Step: 3461, Loss: 0.0285\n",
      "Train Step: 3462, Loss: 0.1676\n",
      "Train Step: 3463, Loss: 0.1309\n",
      "Train Step: 3464, Loss: 0.1396\n",
      "Train Step: 3465, Loss: 0.0626\n",
      "Train Step: 3466, Loss: 0.0820\n",
      "Train Step: 3467, Loss: 0.1916\n",
      "Train Step: 3468, Loss: 0.2410\n",
      "Train Step: 3469, Loss: 0.1402\n",
      "Train Step: 3470, Loss: 0.0750\n",
      "Train Step: 3471, Loss: 0.1071\n",
      "Train Step: 3472, Loss: 0.0464\n",
      "Train Step: 3473, Loss: 0.0652\n",
      "Train Step: 3474, Loss: 0.0487\n",
      "Train Step: 3475, Loss: 0.0194\n",
      "Train Step: 3476, Loss: 0.0622\n",
      "Train Step: 3477, Loss: 0.1068\n",
      "Train Step: 3478, Loss: 0.0743\n",
      "Train Step: 3479, Loss: 0.0962\n",
      "Train Step: 3480, Loss: 0.1166\n",
      "Train Step: 3481, Loss: 0.0769\n",
      "Train Step: 3482, Loss: 0.0381\n",
      "Train Step: 3483, Loss: 0.0893\n",
      "Train Step: 3484, Loss: 0.0684\n",
      "Train Step: 3485, Loss: 0.0973\n",
      "Train Step: 3486, Loss: 0.1381\n",
      "Train Step: 3487, Loss: 0.0575\n",
      "Train Step: 3488, Loss: 0.2201\n",
      "Train Step: 3489, Loss: 0.0718\n",
      "Train Step: 3490, Loss: 0.0672\n",
      "Train Step: 3491, Loss: 0.1090\n",
      "Train Step: 3492, Loss: 0.1822\n",
      "Train Step: 3493, Loss: 0.0721\n",
      "Train Step: 3494, Loss: 0.1069\n",
      "Train Step: 3495, Loss: 0.0927\n",
      "Train Step: 3496, Loss: 0.1375\n",
      "Train Step: 3497, Loss: 0.0555\n",
      "Train Step: 3498, Loss: 0.0588\n",
      "Train Step: 3499, Loss: 0.1221\n",
      "Train Step: 3500, Loss: 0.0812\n",
      "Train Step: 3501, Loss: 0.0622\n",
      "Train Step: 3502, Loss: 0.0766\n",
      "Train Step: 3503, Loss: 0.0761\n",
      "Train Step: 3504, Loss: 0.1023\n",
      "Train Step: 3505, Loss: 0.1312\n",
      "Train Step: 3506, Loss: 0.1448\n",
      "Train Step: 3507, Loss: 0.1753\n",
      "Train Step: 3508, Loss: 0.0923\n",
      "Train Step: 3509, Loss: 0.1222\n",
      "Train Step: 3510, Loss: 0.0619\n",
      "Train Step: 3511, Loss: 0.0859\n",
      "Train Step: 3512, Loss: 0.0449\n",
      "Train Step: 3513, Loss: 0.0475\n",
      "Train Step: 3514, Loss: 0.0892\n",
      "Train Step: 3515, Loss: 0.1269\n",
      "Train Step: 3516, Loss: 0.0420\n",
      "Train Step: 3517, Loss: 0.0600\n",
      "Train Step: 3518, Loss: 0.0691\n",
      "Train Step: 3519, Loss: 0.0841\n",
      "Epoch [9/10], Loss: 0.0914\n",
      "Train Step: 3520, Loss: 0.1403\n",
      "Train Step: 3521, Loss: 0.0431\n",
      "Train Step: 3522, Loss: 0.0545\n",
      "Train Step: 3523, Loss: 0.1521\n",
      "Train Step: 3524, Loss: 0.0787\n",
      "Train Step: 3525, Loss: 0.0641\n",
      "Train Step: 3526, Loss: 0.0764\n",
      "Train Step: 3527, Loss: 0.0611\n",
      "Train Step: 3528, Loss: 0.0604\n",
      "Train Step: 3529, Loss: 0.0398\n",
      "Train Step: 3530, Loss: 0.0289\n",
      "Train Step: 3531, Loss: 0.0813\n",
      "Train Step: 3532, Loss: 0.0415\n",
      "Train Step: 3533, Loss: 0.0497\n",
      "Train Step: 3534, Loss: 0.0740\n",
      "Train Step: 3535, Loss: 0.0549\n",
      "Train Step: 3536, Loss: 0.0228\n",
      "Train Step: 3537, Loss: 0.0591\n",
      "Train Step: 3538, Loss: 0.0159\n",
      "Train Step: 3539, Loss: 0.0398\n",
      "Train Step: 3540, Loss: 0.0518\n",
      "Train Step: 3541, Loss: 0.0218\n",
      "Train Step: 3542, Loss: 0.0796\n",
      "Train Step: 3543, Loss: 0.0391\n",
      "Train Step: 3544, Loss: 0.0446\n",
      "Train Step: 3545, Loss: 0.0522\n",
      "Train Step: 3546, Loss: 0.0533\n",
      "Train Step: 3547, Loss: 0.0412\n",
      "Train Step: 3548, Loss: 0.0389\n",
      "Train Step: 3549, Loss: 0.0572\n",
      "Train Step: 3550, Loss: 0.0381\n",
      "Train Step: 3551, Loss: 0.0384\n",
      "Train Step: 3552, Loss: 0.0165\n",
      "Train Step: 3553, Loss: 0.0473\n",
      "Train Step: 3554, Loss: 0.0372\n",
      "Train Step: 3555, Loss: 0.1060\n",
      "Train Step: 3556, Loss: 0.0911\n",
      "Train Step: 3557, Loss: 0.0466\n",
      "Train Step: 3558, Loss: 0.0645\n",
      "Train Step: 3559, Loss: 0.0614\n",
      "Train Step: 3560, Loss: 0.0451\n",
      "Train Step: 3561, Loss: 0.0473\n",
      "Train Step: 3562, Loss: 0.0263\n",
      "Train Step: 3563, Loss: 0.0772\n",
      "Train Step: 3564, Loss: 0.0497\n",
      "Train Step: 3565, Loss: 0.0454\n",
      "Train Step: 3566, Loss: 0.0463\n",
      "Train Step: 3567, Loss: 0.0242\n",
      "Train Step: 3568, Loss: 0.0221\n",
      "Train Step: 3569, Loss: 0.0704\n",
      "Train Step: 3570, Loss: 0.0527\n",
      "Train Step: 3571, Loss: 0.0519\n",
      "Train Step: 3572, Loss: 0.0578\n",
      "Train Step: 3573, Loss: 0.0932\n",
      "Train Step: 3574, Loss: 0.0305\n",
      "Train Step: 3575, Loss: 0.0956\n",
      "Train Step: 3576, Loss: 0.0832\n",
      "Train Step: 3577, Loss: 0.0724\n",
      "Train Step: 3578, Loss: 0.1382\n",
      "Train Step: 3579, Loss: 0.0768\n",
      "Train Step: 3580, Loss: 0.0670\n",
      "Train Step: 3581, Loss: 0.0387\n",
      "Train Step: 3582, Loss: 0.0972\n",
      "Train Step: 3583, Loss: 0.0159\n",
      "Train Step: 3584, Loss: 0.0223\n",
      "Train Step: 3585, Loss: 0.0288\n",
      "Train Step: 3586, Loss: 0.0792\n",
      "Train Step: 3587, Loss: 0.0305\n",
      "Train Step: 3588, Loss: 0.0513\n",
      "Train Step: 3589, Loss: 0.0383\n",
      "Train Step: 3590, Loss: 0.0929\n",
      "Train Step: 3591, Loss: 0.0257\n",
      "Train Step: 3592, Loss: 0.0459\n",
      "Train Step: 3593, Loss: 0.0244\n",
      "Train Step: 3594, Loss: 0.0484\n",
      "Train Step: 3595, Loss: 0.0271\n",
      "Train Step: 3596, Loss: 0.0455\n",
      "Train Step: 3597, Loss: 0.0514\n",
      "Train Step: 3598, Loss: 0.0654\n",
      "Train Step: 3599, Loss: 0.0546\n",
      "Train Step: 3600, Loss: 0.0732\n",
      "Train Step: 3601, Loss: 0.0472\n",
      "Train Step: 3602, Loss: 0.0259\n",
      "Train Step: 3603, Loss: 0.0326\n",
      "Train Step: 3604, Loss: 0.1100\n",
      "Train Step: 3605, Loss: 0.0406\n",
      "Train Step: 3606, Loss: 0.0156\n",
      "Train Step: 3607, Loss: 0.0327\n",
      "Train Step: 3608, Loss: 0.0383\n",
      "Train Step: 3609, Loss: 0.0315\n",
      "Train Step: 3610, Loss: 0.0898\n",
      "Train Step: 3611, Loss: 0.0475\n",
      "Train Step: 3612, Loss: 0.0616\n",
      "Train Step: 3613, Loss: 0.0637\n",
      "Train Step: 3614, Loss: 0.0611\n",
      "Train Step: 3615, Loss: 0.0286\n",
      "Train Step: 3616, Loss: 0.0205\n",
      "Train Step: 3617, Loss: 0.0554\n",
      "Train Step: 3618, Loss: 0.0173\n",
      "Train Step: 3619, Loss: 0.0445\n",
      "Train Step: 3620, Loss: 0.0443\n",
      "Train Step: 3621, Loss: 0.0913\n",
      "Train Step: 3622, Loss: 0.0204\n",
      "Train Step: 3623, Loss: 0.0320\n",
      "Train Step: 3624, Loss: 0.0286\n",
      "Train Step: 3625, Loss: 0.0406\n",
      "Train Step: 3626, Loss: 0.0701\n",
      "Train Step: 3627, Loss: 0.1799\n",
      "Train Step: 3628, Loss: 0.0635\n",
      "Train Step: 3629, Loss: 0.0929\n",
      "Train Step: 3630, Loss: 0.0437\n",
      "Train Step: 3631, Loss: 0.1113\n",
      "Train Step: 3632, Loss: 0.0481\n",
      "Train Step: 3633, Loss: 0.0412\n",
      "Train Step: 3634, Loss: 0.1239\n",
      "Train Step: 3635, Loss: 0.0241\n",
      "Train Step: 3636, Loss: 0.0872\n",
      "Train Step: 3637, Loss: 0.1087\n",
      "Train Step: 3638, Loss: 0.0580\n",
      "Train Step: 3639, Loss: 0.0559\n",
      "Train Step: 3640, Loss: 0.0877\n",
      "Train Step: 3641, Loss: 0.1589\n",
      "Train Step: 3642, Loss: 0.0462\n",
      "Train Step: 3643, Loss: 0.0237\n",
      "Train Step: 3644, Loss: 0.0560\n",
      "Train Step: 3645, Loss: 0.0542\n",
      "Train Step: 3646, Loss: 0.1342\n",
      "Train Step: 3647, Loss: 0.0284\n",
      "Train Step: 3648, Loss: 0.1965\n",
      "Train Step: 3649, Loss: 0.0178\n",
      "Train Step: 3650, Loss: 0.0278\n",
      "Train Step: 3651, Loss: 0.0471\n",
      "Train Step: 3652, Loss: 0.0205\n",
      "Train Step: 3653, Loss: 0.0824\n",
      "Train Step: 3654, Loss: 0.0491\n",
      "Train Step: 3655, Loss: 0.0183\n",
      "Train Step: 3656, Loss: 0.0275\n",
      "Train Step: 3657, Loss: 0.0315\n",
      "Train Step: 3658, Loss: 0.1104\n",
      "Train Step: 3659, Loss: 0.0208\n",
      "Train Step: 3660, Loss: 0.0731\n",
      "Train Step: 3661, Loss: 0.0365\n",
      "Train Step: 3662, Loss: 0.1467\n",
      "Train Step: 3663, Loss: 0.0706\n",
      "Train Step: 3664, Loss: 0.0746\n",
      "Train Step: 3665, Loss: 0.0171\n",
      "Train Step: 3666, Loss: 0.1002\n",
      "Train Step: 3667, Loss: 0.0335\n",
      "Train Step: 3668, Loss: 0.1189\n",
      "Train Step: 3669, Loss: 0.1171\n",
      "Train Step: 3670, Loss: 0.0539\n",
      "Train Step: 3671, Loss: 0.0501\n",
      "Train Step: 3672, Loss: 0.0838\n",
      "Train Step: 3673, Loss: 0.1072\n",
      "Train Step: 3674, Loss: 0.0407\n",
      "Train Step: 3675, Loss: 0.0688\n",
      "Train Step: 3676, Loss: 0.2417\n",
      "Train Step: 3677, Loss: 0.1037\n",
      "Train Step: 3678, Loss: 0.0615\n",
      "Train Step: 3679, Loss: 0.0182\n",
      "Train Step: 3680, Loss: 0.0484\n",
      "Train Step: 3681, Loss: 0.1361\n",
      "Train Step: 3682, Loss: 0.0635\n",
      "Train Step: 3683, Loss: 0.0369\n",
      "Train Step: 3684, Loss: 0.0829\n",
      "Train Step: 3685, Loss: 0.0447\n",
      "Train Step: 3686, Loss: 0.0439\n",
      "Train Step: 3687, Loss: 0.0982\n",
      "Train Step: 3688, Loss: 0.0607\n",
      "Train Step: 3689, Loss: 0.0439\n",
      "Train Step: 3690, Loss: 0.0870\n",
      "Train Step: 3691, Loss: 0.0574\n",
      "Train Step: 3692, Loss: 0.0198\n",
      "Train Step: 3693, Loss: 0.1375\n",
      "Train Step: 3694, Loss: 0.0308\n",
      "Train Step: 3695, Loss: 0.0120\n",
      "Train Step: 3696, Loss: 0.0478\n",
      "Train Step: 3697, Loss: 0.1294\n",
      "Train Step: 3698, Loss: 0.0625\n",
      "Train Step: 3699, Loss: 0.0467\n",
      "Train Step: 3700, Loss: 0.0162\n",
      "Train Step: 3701, Loss: 0.0337\n",
      "Train Step: 3702, Loss: 0.0447\n",
      "Train Step: 3703, Loss: 0.0259\n",
      "Train Step: 3704, Loss: 0.0897\n",
      "Train Step: 3705, Loss: 0.0436\n",
      "Train Step: 3706, Loss: 0.0919\n",
      "Train Step: 3707, Loss: 0.0844\n",
      "Train Step: 3708, Loss: 0.0353\n",
      "Train Step: 3709, Loss: 0.0287\n",
      "Train Step: 3710, Loss: 0.0676\n",
      "Train Step: 3711, Loss: 0.0621\n",
      "Train Step: 3712, Loss: 0.0248\n",
      "Train Step: 3713, Loss: 0.0332\n",
      "Train Step: 3714, Loss: 0.0855\n",
      "Train Step: 3715, Loss: 0.0110\n",
      "Train Step: 3716, Loss: 0.0543\n",
      "Train Step: 3717, Loss: 0.0273\n",
      "Train Step: 3718, Loss: 0.1148\n",
      "Train Step: 3719, Loss: 0.0392\n",
      "Train Step: 3720, Loss: 0.0547\n",
      "Train Step: 3721, Loss: 0.0291\n",
      "Train Step: 3722, Loss: 0.0118\n",
      "Train Step: 3723, Loss: 0.0292\n",
      "Train Step: 3724, Loss: 0.0471\n",
      "Train Step: 3725, Loss: 0.1125\n",
      "Train Step: 3726, Loss: 0.0228\n",
      "Train Step: 3727, Loss: 0.0613\n",
      "Train Step: 3728, Loss: 0.0517\n",
      "Train Step: 3729, Loss: 0.0422\n",
      "Train Step: 3730, Loss: 0.0311\n",
      "Train Step: 3731, Loss: 0.0549\n",
      "Train Step: 3732, Loss: 0.0778\n",
      "Train Step: 3733, Loss: 0.0685\n",
      "Train Step: 3734, Loss: 0.1070\n",
      "Train Step: 3735, Loss: 0.0589\n",
      "Train Step: 3736, Loss: 0.0242\n",
      "Train Step: 3737, Loss: 0.0694\n",
      "Train Step: 3738, Loss: 0.1253\n",
      "Train Step: 3739, Loss: 0.0354\n",
      "Train Step: 3740, Loss: 0.0300\n",
      "Train Step: 3741, Loss: 0.0423\n",
      "Train Step: 3742, Loss: 0.0687\n",
      "Train Step: 3743, Loss: 0.0252\n",
      "Train Step: 3744, Loss: 0.0317\n",
      "Train Step: 3745, Loss: 0.1910\n",
      "Train Step: 3746, Loss: 0.0828\n",
      "Train Step: 3747, Loss: 0.0377\n",
      "Train Step: 3748, Loss: 0.0512\n",
      "Train Step: 3749, Loss: 0.0164\n",
      "Train Step: 3750, Loss: 0.0546\n",
      "Train Step: 3751, Loss: 0.0507\n",
      "Train Step: 3752, Loss: 0.0777\n",
      "Train Step: 3753, Loss: 0.0298\n",
      "Train Step: 3754, Loss: 0.0137\n",
      "Train Step: 3755, Loss: 0.1186\n",
      "Train Step: 3756, Loss: 0.0311\n",
      "Train Step: 3757, Loss: 0.0537\n",
      "Train Step: 3758, Loss: 0.0191\n",
      "Train Step: 3759, Loss: 0.1729\n",
      "Train Step: 3760, Loss: 0.0362\n",
      "Train Step: 3761, Loss: 0.0502\n",
      "Train Step: 3762, Loss: 0.1196\n",
      "Train Step: 3763, Loss: 0.0636\n",
      "Train Step: 3764, Loss: 0.2350\n",
      "Train Step: 3765, Loss: 0.0859\n",
      "Train Step: 3766, Loss: 0.0591\n",
      "Train Step: 3767, Loss: 0.0243\n",
      "Train Step: 3768, Loss: 0.0271\n",
      "Train Step: 3769, Loss: 0.0358\n",
      "Train Step: 3770, Loss: 0.0226\n",
      "Train Step: 3771, Loss: 0.0192\n",
      "Train Step: 3772, Loss: 0.0688\n",
      "Train Step: 3773, Loss: 0.1657\n",
      "Train Step: 3774, Loss: 0.0179\n",
      "Train Step: 3775, Loss: 0.0201\n",
      "Train Step: 3776, Loss: 0.0348\n",
      "Train Step: 3777, Loss: 0.0611\n",
      "Train Step: 3778, Loss: 0.0921\n",
      "Train Step: 3779, Loss: 0.0571\n",
      "Train Step: 3780, Loss: 0.1346\n",
      "Train Step: 3781, Loss: 0.0168\n",
      "Train Step: 3782, Loss: 0.0291\n",
      "Train Step: 3783, Loss: 0.0166\n",
      "Train Step: 3784, Loss: 0.0557\n",
      "Train Step: 3785, Loss: 0.0855\n",
      "Train Step: 3786, Loss: 0.1396\n",
      "Train Step: 3787, Loss: 0.0790\n",
      "Train Step: 3788, Loss: 0.0737\n",
      "Train Step: 3789, Loss: 0.1032\n",
      "Train Step: 3790, Loss: 0.0957\n",
      "Train Step: 3791, Loss: 0.0562\n",
      "Train Step: 3792, Loss: 0.0844\n",
      "Train Step: 3793, Loss: 0.1113\n",
      "Train Step: 3794, Loss: 0.0466\n",
      "Train Step: 3795, Loss: 0.0175\n",
      "Train Step: 3796, Loss: 0.0851\n",
      "Train Step: 3797, Loss: 0.0184\n",
      "Train Step: 3798, Loss: 0.0200\n",
      "Train Step: 3799, Loss: 0.0574\n",
      "Train Step: 3800, Loss: 0.0804\n",
      "Train Step: 3801, Loss: 0.0278\n",
      "Train Step: 3802, Loss: 0.0636\n",
      "Train Step: 3803, Loss: 0.0634\n",
      "Train Step: 3804, Loss: 0.0909\n",
      "Train Step: 3805, Loss: 0.0401\n",
      "Train Step: 3806, Loss: 0.0244\n",
      "Train Step: 3807, Loss: 0.0623\n",
      "Train Step: 3808, Loss: 0.0613\n",
      "Train Step: 3809, Loss: 0.0235\n",
      "Train Step: 3810, Loss: 0.1326\n",
      "Train Step: 3811, Loss: 0.0752\n",
      "Train Step: 3812, Loss: 0.0663\n",
      "Train Step: 3813, Loss: 0.0464\n",
      "Train Step: 3814, Loss: 0.0445\n",
      "Train Step: 3815, Loss: 0.0994\n",
      "Train Step: 3816, Loss: 0.0165\n",
      "Train Step: 3817, Loss: 0.0557\n",
      "Train Step: 3818, Loss: 0.0997\n",
      "Train Step: 3819, Loss: 0.1277\n",
      "Train Step: 3820, Loss: 0.0443\n",
      "Train Step: 3821, Loss: 0.0760\n",
      "Train Step: 3822, Loss: 0.0272\n",
      "Train Step: 3823, Loss: 0.1012\n",
      "Train Step: 3824, Loss: 0.0971\n",
      "Train Step: 3825, Loss: 0.0666\n",
      "Train Step: 3826, Loss: 0.0811\n",
      "Train Step: 3827, Loss: 0.0431\n",
      "Train Step: 3828, Loss: 0.0739\n",
      "Train Step: 3829, Loss: 0.0566\n",
      "Train Step: 3830, Loss: 0.0469\n",
      "Train Step: 3831, Loss: 0.0292\n",
      "Train Step: 3832, Loss: 0.1074\n",
      "Train Step: 3833, Loss: 0.0262\n",
      "Train Step: 3834, Loss: 0.0501\n",
      "Train Step: 3835, Loss: 0.0459\n",
      "Train Step: 3836, Loss: 0.0640\n",
      "Train Step: 3837, Loss: 0.1695\n",
      "Train Step: 3838, Loss: 0.0485\n",
      "Train Step: 3839, Loss: 0.0369\n",
      "Train Step: 3840, Loss: 0.0376\n",
      "Train Step: 3841, Loss: 0.0400\n",
      "Train Step: 3842, Loss: 0.0443\n",
      "Train Step: 3843, Loss: 0.0451\n",
      "Train Step: 3844, Loss: 0.1054\n",
      "Train Step: 3845, Loss: 0.0645\n",
      "Train Step: 3846, Loss: 0.0293\n",
      "Train Step: 3847, Loss: 0.0302\n",
      "Train Step: 3848, Loss: 0.1457\n",
      "Train Step: 3849, Loss: 0.0714\n",
      "Train Step: 3850, Loss: 0.0299\n",
      "Train Step: 3851, Loss: 0.1672\n",
      "Train Step: 3852, Loss: 0.1021\n",
      "Train Step: 3853, Loss: 0.0660\n",
      "Train Step: 3854, Loss: 0.0376\n",
      "Train Step: 3855, Loss: 0.0384\n",
      "Train Step: 3856, Loss: 0.1456\n",
      "Train Step: 3857, Loss: 0.0263\n",
      "Train Step: 3858, Loss: 0.0342\n",
      "Train Step: 3859, Loss: 0.0785\n",
      "Train Step: 3860, Loss: 0.0554\n",
      "Train Step: 3861, Loss: 0.0247\n",
      "Train Step: 3862, Loss: 0.0309\n",
      "Train Step: 3863, Loss: 0.0981\n",
      "Train Step: 3864, Loss: 0.0818\n",
      "Train Step: 3865, Loss: 0.0362\n",
      "Train Step: 3866, Loss: 0.1129\n",
      "Train Step: 3867, Loss: 0.0120\n",
      "Train Step: 3868, Loss: 0.0681\n",
      "Train Step: 3869, Loss: 0.0271\n",
      "Train Step: 3870, Loss: 0.0460\n",
      "Train Step: 3871, Loss: 0.0086\n",
      "Train Step: 3872, Loss: 0.0998\n",
      "Train Step: 3873, Loss: 0.0177\n",
      "Train Step: 3874, Loss: 0.0918\n",
      "Train Step: 3875, Loss: 0.0345\n",
      "Train Step: 3876, Loss: 0.0462\n",
      "Train Step: 3877, Loss: 0.0420\n",
      "Train Step: 3878, Loss: 0.0309\n",
      "Train Step: 3879, Loss: 0.1092\n",
      "Train Step: 3880, Loss: 0.1365\n",
      "Train Step: 3881, Loss: 0.0401\n",
      "Train Step: 3882, Loss: 0.0264\n",
      "Train Step: 3883, Loss: 0.1500\n",
      "Train Step: 3884, Loss: 0.0755\n",
      "Train Step: 3885, Loss: 0.0619\n",
      "Train Step: 3886, Loss: 0.0304\n",
      "Train Step: 3887, Loss: 0.0201\n",
      "Train Step: 3888, Loss: 0.0104\n",
      "Train Step: 3889, Loss: 0.0697\n",
      "Train Step: 3890, Loss: 0.0698\n",
      "Train Step: 3891, Loss: 0.0739\n",
      "Train Step: 3892, Loss: 0.0888\n",
      "Train Step: 3893, Loss: 0.0432\n",
      "Train Step: 3894, Loss: 0.0871\n",
      "Train Step: 3895, Loss: 0.0266\n",
      "Train Step: 3896, Loss: 0.0947\n",
      "Train Step: 3897, Loss: 0.0793\n",
      "Train Step: 3898, Loss: 0.0602\n",
      "Train Step: 3899, Loss: 0.0326\n",
      "Train Step: 3900, Loss: 0.0389\n",
      "Train Step: 3901, Loss: 0.1222\n",
      "Train Step: 3902, Loss: 0.0483\n",
      "Train Step: 3903, Loss: 0.0518\n",
      "Train Step: 3904, Loss: 0.0486\n",
      "Train Step: 3905, Loss: 0.0896\n",
      "Train Step: 3906, Loss: 0.0578\n",
      "Train Step: 3907, Loss: 0.0480\n",
      "Train Step: 3908, Loss: 0.0362\n",
      "Train Step: 3909, Loss: 0.0584\n",
      "Train Step: 3910, Loss: 0.0542\n",
      "Epoch [10/10], Loss: 0.0608\n",
      "Training Time: 522.55 seconds\n",
      "Total Train Steps: 3910\n",
      "Inference Time: 37.15 seconds\n",
      "Accuracy: 0.5186\n",
      "Precision: 0.5337\n",
      "Recall: 0.5186\n",
      "F1-score: 0.4580\n",
      "Training Time: 522.55 seconds\n",
      "Inference Time: 37.15 seconds\n",
      "Confusion Matrix:\n",
      " [[ 2302 10198]\n",
      " [ 1836 10664]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.18      0.28     12500\n",
      "           1       0.51      0.85      0.64     12500\n",
      "\n",
      "    accuracy                           0.52     25000\n",
      "   macro avg       0.53      0.52      0.46     25000\n",
      "weighted avg       0.53      0.52      0.46     25000\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 2 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 主函数\n",
    "def main():\n",
    "    # 参数设置\n",
    "    batch_size = 64\n",
    "    embedding_dim = 100\n",
    "    kernel_sizes = [3, 4, 5]\n",
    "    num_channels = 100\n",
    "    num_classes = 2\n",
    "    num_epochs = 10\n",
    "\n",
    " # 加载数据\n",
    "    train_loader = load_data(aclImdb_data_dir, 'train', batch_size)\n",
    "    test_loader = load_data(aclImdb_data_dir, 'test', batch_size)\n",
    "      # 构建词汇表并获取词汇表大小\n",
    "    dataset = CustomIMDbDataset(aclImdb_data_dir, 'train')\n",
    "    vocab_size = len(dataset.vocab)\n",
    "\n",
    "    # 初始化模型、损失函数和优化器\n",
    "    model = TextCNN(vocab_size, embedding_dim, kernel_sizes, num_channels, num_classes).to(device)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 训练模型\n",
    "    start_time = time.time()\n",
    "    total_train_steps = 0  # 添加训练次数计数器\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        model.train()\n",
    "        epoch_loss = 0.0\n",
    "        for batch in train_loader:\n",
    "            # 处理批次数据\n",
    "            batch_data = []\n",
    "            batch_labels = []\n",
    "            for item in batch:\n",
    "                text, label = item\n",
    "                batch_data.append(text)\n",
    "                batch_labels.append(label)\n",
    "            # 填充文本数据以使它们具有相同的长度\n",
    "            max_len = max(len(text) for text in batch_data)\n",
    "            padded_batch = []\n",
    "            for text in batch_data:\n",
    "                padded_text = text.tolist() + [vocab_size] * (max_len - len(text))\n",
    "                padded_batch.append(padded_text)\n",
    "            batch_data = torch.tensor(padded_batch, dtype=torch.long).to(device)\n",
    "            batch_labels = torch.tensor(batch_labels, dtype=torch.long).to(device)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(batch_data)\n",
    "            loss = criterion(outputs, batch_labels)\n",
    "            epoch_loss += loss.item()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            total_train_steps += 1  # 增加训练次数计数器\n",
    "            print(f'Train Step: {total_train_steps}, Loss: {loss.item():.4f}')  # 输出训练次数和损失\n",
    "\n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss / len(train_loader):.4f}')\n",
    "\n",
    "    train_time = time.time() - start_time\n",
    "    print(f\"Training Time: {train_time:.2f} seconds\")\n",
    "    print(f\"Total Train Steps: {total_train_steps}\")  # 输出总训练次数\n",
    "     # 测试模型\n",
    "    model.eval()\n",
    "    start_inference = time.time()\n",
    "\n",
    "    all_labels = []\n",
    "    all_preds = []\n",
    "    with torch.no_grad():\n",
    "        for batch in test_loader:\n",
    "            batch_data = []\n",
    "            batch_labels = []\n",
    "            for item in batch:\n",
    "                text, label = item\n",
    "                batch_data.append(text)\n",
    "                batch_labels.append(label)\n",
    "            max_len = max(len(text) for text in batch_data)\n",
    "            padded_batch = []\n",
    "            for text in batch_data:\n",
    "                padded_text = text.tolist() + [vocab_size] * (max_len - len(text))\n",
    "                padded_batch.append(padded_text)\n",
    "            batch_data = torch.tensor(padded_batch, dtype=torch.long).to(device)\n",
    "            batch_labels = torch.tensor(batch_labels, dtype=torch.long).to(device)\n",
    "\n",
    "            outputs = model(batch_data)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            all_labels.extend(batch_labels.cpu().numpy())\n",
    "            all_preds.extend(predicted.cpu().numpy())\n",
    "\n",
    "    inference_time = time.time() - start_inference\n",
    "    print(f\"Inference Time: {inference_time:.2f} seconds\")\n",
    "      # 计算评估指标\n",
    "    accuracy = accuracy_score(all_labels, all_preds)\n",
    "    precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average='weighted')\n",
    "    conf_matrix = confusion_matrix(all_labels, all_preds)\n",
    "    class_report = classification_report(all_labels, all_preds)\n",
    "\n",
    "    # 输出结果\n",
    "    print(f\"Accuracy: {accuracy:.4f}\")\n",
    "    print(f\"Precision: {precision:.4f}\")\n",
    "    print(f\"Recall: {recall:.4f}\")\n",
    "    print(f\"F1-score: {f1:.4f}\")\n",
    "    print(f\"Training Time: {train_time:.2f} seconds\")\n",
    "    print(f\"Inference Time: {inference_time:.2f} seconds\")\n",
    "    print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "    print(\"Classification Report:\\n\", class_report)\n",
    "      # 绘制混淆矩阵热力图\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "    plt.title(\"CNN - Confusion Matrix (IMDb)\")\n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.ylabel(\"True Label\")\n",
    "    plt.show()\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-11T02:39:01.690957Z",
     "start_time": "2025-06-11T02:27:35.576678Z"
    }
   },
   "id": "a18431ef94acc94f",
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (2521755834.py, line 2)",
     "output_type": "error",
     "traceback": [
      "  \u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[15]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[31m    \u001B[39m\u001B[31mplt.figure(figsize=(8, 6))\u001B[39m\n    ^\n\u001B[31mIndentationError\u001B[39m\u001B[31m:\u001B[39m unexpected indent\n"
     ]
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-11T02:27:26.269746Z",
     "start_time": "2025-06-11T02:27:26.254225Z"
    }
   },
   "id": "21c2642f9e2b0fe2",
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "3a54d03ee23eab90"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
